%0 Journal Article %A Aguero, F %A Al-Lazikani, B %A Aslett, M %A Berriman, M %A Buckner, FS %A Campbell, RK %A Carmona, S %A Carruthers, IM %A Chan, AW %A Chen, F %A Crowther, GJ %A Doyle, MA %A Hertz-Fowler, C %A Hopkins, AL %A McAllister, G %A Nwaka, S %A Overington, JP %A Pain, A %A Paolini, GV %A Pieper, U %A Ralph, SA %A Riechers, A %A Roos, DS %A Sali, A %A Shanmugam, D %A Suzuki, T %A Van Voorhis, WC %A Verlinde, CL %D 2008 %T Genomic-scale prioritization of drug targets: the TDR Targets database %B Nat Rev Drug Discov %V 7 %N 11 %P 900-907 %8 Nov %! Genomic-scale prioritization of drug targets: the TDR Targets database. %@ 1474-1784 %2 PMCID3184002 %M 18927591;PMCID:PMC3184002 %L 202 %F 202 %K Animals Communicable Diseases Databases, Genetic Drug Design Genome Humans %X The increasing availability of genomic data for pathogens that cause tropical diseases has created new opportunities for drug discovery and development. However, if the potential of such data is to be fully exploited, the data must be effectively integrated and be easy to interrogate. Here, we discuss the development of the TDR Targets database (http://tdrtargets.org), which encompasses extensive genetic, biochemical and pharmacological data related to tropical disease pathogens, as well as computationally predicted druggability for potential targets and compound desirability information. By allowing the integration and weighting of this information, this database aims to facilitate the identification and prioritization of candidate drug targets for pathogens. %U http://salilab.org/pdf/Aguero_NatureReviewsDrugDiscovery_2008.pdf %+ Instituto de Investigaciones Biotecnol‚àö√µgicas, Universidad Nacional de General San Mart‚àö√•n, San Mart‚àö√•n 1650, Buenos Aires, Argentina. fernan@unsam.edu.ar %G eng %0 Journal Article %A Akey, C.W. %A Echeverria, I. %A Ouch, C. %A Nudelman, I. %A Shi, Y. %A Wang, J. %A Chait, B.T. %A Sali, A. %A Fernandez-Martinez, J. %A Rout, M.P. %D 2023 %T Implications of a multiscale structure of the yeast nuclear pore complex %B Mol Cell %V 83 %N 18 %P 3283-3302.e5 %! Implications of a multiscale structure of the yeast nuclear pore complex %R 10.1016/j.molcel.2023.08.025 %2 PMCID10630966 %M 37738963 %L 445 %F 445 %0 Journal Article %A Akey, C.W. %A Singh, D. %A Ouch, C. %A Echeverria, I. %A Nudelman, I. %A Varberg, J.M. %A Yu, Z. %A Fang, F. %A Shi, Y. %A Wang, J. %A Salzberg, D. %A Song, K. %A Xu, C. %A Gumbart, J.C. %A Suslov, S. %A Unruh, J. %A Jaspersen, S.L. %A Chait, B.T. %A Sali, A. %A Fernandez-Martinez, J. %A Ludtke, S.J. %A Villa, E. %A Rout, M.P. %D 2022 %T Comprehensive Structure and Functional Adaptations of the Yeast Nuclear Pore Complex %B Cell %V 185 %N 2 %P 361-378 %! Comprehensive Structure and Functional Adaptations of the Yeast Nuclear Pore Complex %R 10.1016/j.cell.2021.12.015 %2 PMCID8928745 %M 34982960 %L 427 %F 427 %U https://salilab.org/pdf/Akey_Cell_2022.pdf %0 Book Section %A Alber, F. %A Chait, B.T. %A Rout, M.P. %A Sali, A. %D 2008 %T Integrative Structure Determination of Protein Assemblies by Satisfaction of Spatial Restraints %E Panchenko, A. %E Przytycka, T. %B Protein-protein interactions and networks: identification, characterization and prediction. %C London, UK %I Springer-Verlag %P 99-114 %! Integrative Structure Determination of Protein Assemblies by Satisfaction of Spatial Restraints %L 197 %F 197 %Z 6 %U http://salilab.org/pdf/Alber_ProteinProteinInterNet_2008.pdf %0 Journal Article %A Alber, F %A Dokudovskaya, S %A Veenhoff, LM %A Zhang, W %A Kipper, J %A Devos, D %A Suprapto, A %A Karni-Schmidt, O %A Williams, R %A Chait, BT %A Rout, MP %A Sali, A %D 2007 %T Determining the architectures of macromolecular assemblies %B Nature %V 450 %N 7170 %P 683-694 %8 Nov %! Determining the architectures of macromolecular assemblies. %@ 1476-4687 %M 18046405 %L 191 %F 191 %K Cell Survival Computational Biology Macromolecular Substances Microscopy, Immunoelectron Models, Biological Nuclear Pore Nuclear Pore Complex Proteins Protein Binding Proteomics Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Sensitivity and Specificity Uncertainty %X To understand the workings of a living cell, we need to know the architectures of its macromolecular assemblies. Here we show how proteomic data can be used to determine such structures. The process involves the collection of sufficient and diverse high-quality data, translation of these data into spatial restraints, and an optimization that uses the restraints to generate an ensemble of structures consistent with the data. Analysis of the ensemble produces a detailed architectural map of the assembly. We developed our approach on a challenging model system, the nuclear pore complex (NPC). The NPC acts as a dynamic barrier, controlling access to and from the nucleus, and in yeast is a 50 MDa assembly of 456 proteins. The resulting structure, presented in an accompanying paper, reveals the configuration of the proteins in the NPC, providing insights into its evolution and architectural principles. The present approach should be applicable to many other macromolecular assemblies. %U http://salilab.org/pdf/Alber_Nature_2007a.pdf %+ Department of Bioengineering and Therapeutic Sciences, and California Institute for Quantitative Biosciences, Byers Hall, Suite 503B, 1700 4th Street, University of California at San Francisco, San Francisco, California 94158-2330, USA. %G eng %0 Journal Article %A Alber, F %A Dokudovskaya, S %A Veenhoff, LM %A Zhang, W %A Kipper, J %A Devos, D %A Suprapto, A %A Karni-Schmidt, O %A Williams, R %A Chait, BT %A Sali, A %A Rout, MP %D 2007 %T The molecular architecture of the nuclear pore complex %B Nature %V 450 %N 7170 %P 695-701 %8 Nov %! The molecular architecture of the nuclear pore complex. %@ 1476-4687 %M 18046406 %L 190 %F 190 %K Active Transport, Cell Nucleus Binding Sites Cell Nucleus Cytoplasm Evolution, Molecular Macromolecular Substances Nuclear Envelope Nuclear Pore Nuclear Pore Complex Proteins Protein Conformation Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins %X Nuclear pore complexes (NPCs) are proteinaceous assemblies of approximately 50 MDa that selectively transport cargoes across the nuclear envelope. To determine the molecular architecture of the yeast NPC, we collected a diverse set of biophysical and proteomic data, and developed a method for using these data to localize the NPC's 456 constituent proteins (see the accompanying paper). Our structure reveals that half of the NPC is made up of a core scaffold, which is structurally analogous to vesicle-coating complexes. This scaffold forms an interlaced network that coats the entire curved surface of the nuclear envelope membrane within which the NPC is embedded. The selective barrier for transport is formed by large numbers of proteins with disordered regions that line the inner face of the scaffold. The NPC consists of only a few structural modules that resemble each other in terms of the configuration of their homologous constituents, the most striking of these being a 16-fold repetition of 'columns'. These findings provide clues to the evolutionary origins of the NPC. %U http://www.salilab.org/pdf/Alber_Nature_2007.pdf %+ Department of Bioengineering and Therapeutic Sciences, and California Institute for Quantitative Biosciences, Mission Bay QB3, 1700 4th Street, Suite 503B, University of California at San Francisco, San Francisco, California 94158-2330, USA. %G eng %0 Journal Article %A Alber, F %A Eswar, N. %A Sali, A. %D 2004 %T Structure determination of macromolecular complexes by experiment and computation %B Practical Bioinformatics, Ed: J.Bujnicki %V 15 %P 73-96 %8 2003/// %! Structure determination of macromolecular complexes by experiment and computation %L 132 %F 132 %Z TY - JOUR %U http://salilab.org/pdf/Alber_PracticalBioinformatics_2004.pdf %0 Journal Article %A Alber, F %A Forster, F %A Korkin, D %A Topf, M %A Sali, A %D 2008 %T Integrating diverse data for structure determination of macromolecular assemblies %B Annu Rev Biochem %V 77 %P 443-477 %! Integrating diverse data for structure determination of macromolecular assemblies. %@ 0066-4154 %M 18318657 %L 199 %F 199 %K Animals Biochemistry Biophysics Humans Macromolecular Substances Magnetic Resonance Spectroscopy Mass Spectrometry Microscopy, Electron Models, Molecular Molecular Conformation Nuclear Pore Reproducibility of Results Saccharomyces cerevisiae Scattering, Radiation X-Rays %X To understand the cell, we need to determine the macromolecular assembly structures, which may consist of tens to hundreds of components. First, we review the varied experimental data that characterize the assemblies at several levels of resolution. We then describe computational methods for generating the structures using these data. To maximize completeness, resolution, accuracy, precision, and efficiency of the structure determination, a computational approach is required that uses spatial information from a variety of experimental methods. We propose such an approach, defined by its three main components: a hierarchical representation of the assembly, a scoring function consisting of spatial restraints derived from experimental data, and an optimization method that generates structures consistent with the data. This approach is illustrated by determining the configuration of the 456 proteins in the nuclear pore complex (NPC) from baker's yeast. With these tools, we are poised to integrate structural information gathered at multiple levels of the biological hierarchy--from atoms to cells--into a common framework. %U http://salilab.org/pdf/Alber_AnnuRevBiochem_2008.pdf %+ Department of Biopharmaceutical Sciences, and California Institute for Quantitative Biosciences, University of California at San Francisco, CA 94158-2330, USA. alber@usc.edu %G eng %0 Journal Article %A Alber, F %A Kim, MF %A Sali, A %D 2005 %T Structural characterization of assemblies from overall shape and subcomplex compositions %B Structure %V 13 %N 3 %P 435-445 %8 Mar %! Structural characterization of assemblies from overall shape and subcomplex compositions. %@ 0969-2126 %M 15766545 %L 146 %F 146 %K Computational Biology Computer Simulation Cryoelectron Microscopy Models, Molecular Multiprotein Complexes Proteasome Endopeptidase Complex Protein Conformation Protein Subunits %X We suggest structure characterization of macromolecular assemblies by combining assembly shape determined by electron cyromicroscopy with information about subunit proximity determined by affinity purification. To achieve this aim, structure characterization is expressed as a problem in satisfaction of spatial restraints that (1) represents subunits as spheres, (2) encodes information about the subunit excluded volume, assembly shape, and pulldowns in a scoring function, and (3) finds subunit configurations that satisfy the input restraints by an optimization of the scoring function. Testing of the approach with model systems suggests its feasibility. %U http://salilab.org/pdf/Alber_Structure_2005.pdf %+ Department of Biopharmaceutical Sciences and, California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, California 94143, USA. %G eng %0 Journal Article %A Algret, R. %A Fernandez-Martinez, J. %A Shi, Y. %A Kim, S.J. %A Pellarin, R. %A Cimermancic, P. %A Cochet, E. %A Sali, A. %A Chait, B. %A Rout, M. %A Dokudovskaya, S. %D 2014 %T Molecular Architecture and Function of the SEA Complex  - a Modulator of the TORC1 Pathway %B Mol Cell Proteomics %V 13 %P 2855-2870 %! Molecular Architecture and Function of the SEA Complex  - a Modulator of the TORC1 Pathway %2 PMCID4223477 %M 25073740;PMCID:PMC4223477 %L 322 %F 322 %U http://salilab.org/pdf/Algret_MolCellProteomics_2014.pdf %0 Journal Article %A Almo, SC %A Bonanno, JB %A Sauder, JM %A Emtage, S %A Dilorenzo, TP %A Malashkevich, V %A Wasserman, SR %A Swaminathan, S %A Eswaramoorthy, S %A Agarwal, R %A Kumaran, D %A Madegowda, M %A Ragumani, S %A Patskovsky, Y %A Alvarado, J %A Ramagopal, UA %A Faber-Barata, J %A Chance, MR %A Sali, A %A Fiser, A %A Zhang, ZY %A Lawrence, DS %A Burley, SK %D 2007 %T Structural genomics of protein phosphatases %B J Struct Funct Genom %V 8 %N 2-3 %P 121-140 %8 Sep %! Structural genomics of protein phosphatases. %@ 1345-711X %M 18058037 %L 185 %F 185 %K Animals Crystallography, X-Ray Genomics Humans Multigene Family Phosphoprotein Phosphatases Sequence Analysis, DNA %X The New York SGX Research Center for Structural Genomics (NYSGXRC) of the NIGMS Protein Structure Initiative (PSI) has applied its high-throughput X-ray crystallographic structure determination platform to systematic studies of all human protein phosphatases and protein phosphatases from biomedically-relevant pathogens. To date, the NYSGXRC has determined structures of 21 distinct protein phosphatases: 14 from human, 2 from mouse, 2 from the pathogen Toxoplasma gondii, 1 from Trypanosoma brucei, the parasite responsible for African sleeping sickness, and 2 from the principal mosquito vector of malaria in Africa, Anopheles gambiae. These structures provide insights into both normal and pathophysiologic processes, including transcriptional regulation, regulation of major signaling pathways, neural development, and type 1 diabetes. In conjunction with the contributions of other international structural genomics consortia, these efforts promise to provide an unprecedented database and materials repository for structure-guided experimental and computational discovery of inhibitors for all classes of protein phosphatases. %U http://salilab.org/pdf/Almo_JStructFunctGenom_2007.pdf %+ Albert Einstein College of Medicine, Bronx, NY, USA. almo@aecom.yu.edu %G eng %0 Journal Article %A Anderson, CM %A Korkin, D %A Smith, DL %A Makovets, S %A Seidel, JJ %A Sali, A %A Blackburn, EH %D 2008 %T Tel2 mediates activation and localization of ATM/Tel1 kinase to a double-strand break %B Genes Dev %V 22 %N 7 %P 854-859 %8 Apr %! Tel2 mediates activation and localization of ATM/Tel1 kinase to a double-strand break. %@ 0890-9369 %2 PMCID2279195 %M 18334620;PMCID:PMC2279195 %L 207 %F 207 %K Blotting, Western Cell Cycle Chromatin Immunoprecipitation Computational Biology DNA Breaks, Double-Stranded DNA Damage Enzyme Activation Intracellular Signaling Peptides and Proteins Mutation Protein Binding Protein-Serine-Threonine Kinases Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Telomere-Binding Proteins %X The kinases ATM and ATR (Tel1 and Mec1 in the yeast Saccharomyces cerevisiae) control the response to DNA damage. We report that S. cerevisiae Tel2 acts at an early step of the TEL1/ATM pathway of DNA damage signaling. We show that Tel1 and Tel2 interact, and that even when Tel1 protein levels are high, this interaction is specifically required for Tel1 localization to a DNA break and its activation of downstream targets. Computational analysis revealed structural homology between Tel2 and Ddc2 (ATRIP in vertebrates), a partner of Mec1, suggesting a common structural principle used by partners of phoshoinositide 3-kinase-like kinases. %U http://salilab.org/pdf/Anderson_GenesDev_2008.pdf %+ Department of Biochemistry, University of California, San Francisco, California 94143, USA. %G eng %0 Journal Article %A Aragues, R %A Sali, A %A Bonet, J %A Marti-Renom, MA %A Oliva, B %D 2007 %T Characterization of protein hubs by inferring interacting motifs from protein interactions %B PLoS Comput Biol %V 3 %N 9 %P 1761-1771 %8 Sep %! Characterization of protein hubs by inferring interacting motifs from protein interactions. %@ 1553-7358 %M 17941705 %L 181 %F 181 %K Amino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation Models, Chemical Models, Molecular Molecular Sequence Data Protein Binding Protein Interaction Mapping Proteins Sequence Analysis, Protein %X The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks. %U http://salilab.org/pdf/Aragues_PLoSComputationalBiology_2007.pdf %+ Structural Bioinformatics Lab (GRIB), Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine (PRBB), Barcelona, Catalonia, Spain. %G eng %0 Journal Article %A Bajaj, K %A Madhusudhan, MS %A Adkar, BV %A Chakrabarti, P %A Ramakrishnan, C %A Sali, A %A Varadarajan, R %D 2007 %T Stereochemical criteria for prediction of the effects of proline mutations on protein stability %B PLoS Comput Biol %V 3 %N 12 %P e241 %8 Dec %! Stereochemical criteria for prediction of the effects of proline mutations on protein stability. %@ 1553-7358 %M 18069886 %L 180 %F 180 %K Amino Acid Sequence Amino Acid Substitution Bacterial Proteins Bacterial Toxins Computer Simulation Models, Chemical Models, Molecular Molecular Sequence Data Mutagenesis, Site-Directed Mutation Protein Conformation Sequence Analysis, Protein Stereoisomerism Structure-Activity Relationship %X When incorporated into a polypeptide chain, proline (Pro) differs from all other naturally occurring amino acid residues in two important respects. The phi dihedral angle of Pro is constrained to values close to -65 degrees and Pro lacks an amide hydrogen. Consequently, mutations which result in introduction of Pro can significantly affect protein stability. In the present work, we describe a procedure to accurately predict the effect of Pro introduction on protein thermodynamic stability. Seventy-seven of the 97 non-Pro amino acid residues in the model protein, CcdB, were individually mutated to Pro, and the in vivo activity of each mutant was characterized. A decision tree to classify the mutation as perturbing or nonperturbing was created by correlating stereochemical properties of mutants to activity data. The stereochemical properties including main chain dihedral angle phi and main chain amide H-bonds (hydrogen bonds) were determined from 3D models of the mutant proteins built using MODELLER. We assessed the performance of the decision tree on a large dataset of 163 single-site Pro mutations of T4 lysozyme, 74 nsSNPs, and 52 other Pro substitutions from the literature. The overall accuracy of this algorithm was found to be 81% in the case of CcdB, 77% in the case of lysozyme, 76% in the case of nsSNPs, and 71% in the case of other Pro substitution data. The accuracy of Pro scanning mutagenesis for secondary structure assignment was also assessed and found to be at best 69%. Our prediction procedure will be useful in annotating uncharacterized nsSNPs of disease-associated proteins and for protein engineering and design. %U http://salilab.org/pdf/Bajaj_PLoSComputationalBiology_2007.pdf %+ Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India. %G eng %0 Journal Article %A Baker, D. %A Sali, A. %D 2001 %T Protein structure prediction and structural genomics %B Science %V 294 %N 5540 %P 93-96 %8 Oct 5 %! Protein structure prediction and structural genomics %M 11588250 %L 103 %F 103 %K Amino Acid Sequence Animals Binding Sites *Computational Biology Computer Simulation Databases, Factual *Genomics Humans Internet *Models, Molecular *Protein Conformation Protein Folding Protein Structure, Tertiary Proteins/*chemistry/genetics/physiology Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Alignment Software Templates, Genetic %X Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics. %Z 0036-8075 Journal Article %U http://salilab.org/pdf/Baker_Science_2001.pdf %+ Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA. dabaker@u.washington.edu. %0 Journal Article %A Baldi, P. %A Borodovsky, M. %A Brunak, S. %A Burge, C. %A Fickett, J. %A Henikoff, S. %A Koonin, E. %A Sali, A. %A Sander, C. %A Stormo, G. %D 1999 %T The Second Georgia Tech International Conference on Bioinformatics: Sequence, Structure and Function %B Bioinformatics %V 15 %P 865-866 %8 1999/// %! The Second Georgia Tech International Conference on Bioinformatics: Sequence, Structure and Function %M 10743552 %L 78 %F 78 %Z TY - JOUR %U http://salilab.org/pdf/Baldi_Bioinformatics_1999.pdf %0 Journal Article %A Baldwin, E. T. %A van Eeuwen, T. %A Hoyos, D. %A Zalevsky, A. %A Tchesnokov, E. P. %A Sánchez, R. %A Miller, B. D. %A Di Stefano, L. H. %A Ruiz, F. X. %A Hancock, M. %A Işik, E. %A Mendez-Dorantes, C. %A Walpole, T. %A Nichols, C. %A Wan, P. %A Riento, K. %A Halls-Kass, R. %A Augustin, M. %A Lammens, A. %A Jestel, A. %A Upla, P. %A Xibinaku, K. %A Congreve, S. %A Hennink, M. %A Rogala, K. B. %A Schneider, A. M. %A Fairman, J. E. %A Christensen, S. M. %A Desrosiers, B. %A Bisacchi, G. S. %A Saunders, O. L. %A Hafeez, N. %A Miao, W. %A Kapeller, R. %A Zaller, D. M. %A Sali, A. %A Weichenrieder, O. %A Burns, K. H. %A Götte, M. %A Rout, M. P. %A Arnold, E. %A Greenbaum, B. D. %A Romero, D. L. %A LaCava, J. %A Taylor, M. S. %D 2023 %T Structures, functions, and adaptations of the human LINE-1 ORF2 protein %B Nature %V 626 %6 7997 %P 194-206 %! Structures, functions, and adaptations of the human LINE-1 ORF2 protein %R 10.1038/s41586-023-06947-z %2 PMCID10830420 %M 38096902 %L 448 %F 448 %U https://salilab.org/pdf/Baldwin_Nature_2023.pdf %0 Journal Article %A Barkan, D.T. %A Cheng, X. %A Celino, H. %A Tran, T. %A Bhandari, A. %A Craik, C.S. %A Sali, A. %A Smythe, M.L. %D 2016 %T Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23 %B BMC Bioinformatics %V 17 %N 1 %P 481 %! Clustering of disulfide-rich peptides provides scaffolds for hit discovery by phage display: application to interleukin-23 %R 10.1186/s12859-016-1350-9 %2 PMCID5120537 %M 27881076 %L 358 %F 358 %U https://salilab.org/pdf/Barkan_BMCBioinformatics_2016.pdf %0 Journal Article %A Barkan, D. %A Hostetter, D. %A Mahrus, S. %A Pieper, U. %A Wells, J. %A Craik, C. %A Sali, A. %D 2010 %T Prediction of Protease Substrates using Sequence and Structure Features %B Bioinformatics %V 26 %P 1714-1722 %! Prediction of Protease Substrates using Sequence and Structure Features %2 PMCID2894511 %M 20505003;PMCID:PMC2894511 %L 236 %F 236 %U http://salilab.org/pdf/Barkan_Bioinformatics_2010.pdf %W https://github.com/salilab/pcss-web %0 Journal Article %A Barrientos, L. G. %A Campos-Olivas, R. %A Louis, J. M. %A Fiser, A. %A Sali, A. %A Gronenborn, A. M. %D 2001 %T 1H, 13C, 15N resonance assignments and fold verification of a circular permuted variant of the potent HIV-inactivating protein cyanovirin-N %B J Biomol NMR %V 19 %N 3 %P 289-290 %8 Mar %! 1H, 13C, 15N resonance assignments and fold verification of a circular permuted variant of the potent HIV-inactivating protein cyanovirin-N %M 11330821 %L 95 %F 95 %K Anti-HIV Agents/*chemistry Bacterial Proteins/*chemistry/genetics Carbon Isotopes Carrier Proteins/*chemistry/genetics Hydrogen Nitrogen Isotopes Nuclear Magnetic Resonance, Biomolecular Peptides, Cyclic/chemistry/genetics Research Support, Non-U.S. Gov't Variation (Genetics) %Z 0925-2738 Letter %U http://salilab.org/pdf/Barrientos_JBiomolNMR_2001.pdf %0 Journal Article %A Beckmann, R. %A Spahn, C. M. T. %A Eswar, N. %A Helmers, J. %A Penczek, P. A. %A Sali, A. %A Frank, J. %A Blobel, G. %D 2001 %T Architecture of the protein-conducting channel associated with the translating 80S ribosome %B Cell %V 107 %N 3 %P 361-372 %! Architecture of the protein-conducting channel associated with the translating 80S ribosome %@ 0092-8674 %M 11701126 %L 99 %F 99 %X In vitro assembled yeast ribosome-nascent chain complexes (RNCs) containing a signal sequence in the nascent chain were immunopurified and reconstituted with the purified protein-conducting channel (PCC) of yeast endoplasmic reticulum, the Sec61 complex. A cryo-EM reconstruction of the RNC-Sec61 complex at 15.4 Angstrom resolution shows a tRNA in the P site. Distinct rRNA elements and proteins of the large ribosomal subunit form four connections with the PCC across a gap of about 10-20 Angstrom. Binding of the PCC influences the position of the highly dynamic rRNA expansion segment 27. The RNC-bound Sec61 complex has a compact appearance and was estimated to be a trimer. We propose a binary model of cotranslational translocation entailing only two basic functional states of the translating ribosome-channel complex. %U http://salilab.org/pdf/Beckmann_Cell_2001.pdf %0 Journal Article %A Belsare, KD %A Wu, H %A Mondal, D %A Bond, A %A Castillo, E %A Jin, J %A Jo, H %A Roush, AE %A Pilla, KB %A Sali, S %A Condello, C %A DeGrado, WF %D 2022 %T Soluble TREM2 inhibits secondary nucleation of Aβ fibrillization and enhances cellular uptake of fibrillar Aβ %B Proc Natl Acad Sci USA %V 119 %N 5 %P e2114486119 %! Soluble TREM2 inhibits secondary nucleation of Aβ fibrillization and enhances cellular uptake of fibrillar Aβ %R 10.1073/pnas.2114486119 %2 PMCID8812518 %M 35082148 %L 429 %F 429 %U https://salilab.org/pdf/Belsare_ProcNatlAcadSciUSA_2022.pdf %0 Journal Article %A Berlin, K. %A Castaneda, C.A. %A Schneidman-Duhovny, D. %A Sali, A. %A Nava-Tudela, A. %A Fushman, D. %D 2013 %T Recovering a representative conformational ensemble from underdetermined macromolecular structural data %B J Am Chem Soc %V 135 %P 16595-16609 %! Recovering a representative conformational ensemble from underdetermined macromolecular structural data %2 PMCID3902174 %M 24093873;PMCID:PMC3902174 %L 313 %F 313 %U http://salilab.org/pdf/Berlin_JAmChemSoc_2013.pdf %0 Journal Article %A Berman, H. M. %A Burley, S. K. %A Chiu, W. %A Sali, A. %A Adzhubei, A. %A Bourne, P. E. %A Bryant, S. H. %A Dunbrack, R. L., Jr. %A Fidelis, K. %A Frank, J. %A Godzik, A. %A Henrick, K. %A Joachimiak, A. %A Heymann, B. %A Jones, D. %A Markley, J. L. %A Moult, J. %A Montelione, G. T. %A Orengo, C. %A Rossmann, M. G. %A Rost, B. %A Saibil, H. %A Schwede, T. %A Standley, D. M. %A Westbrook, J. D. %D 2006 %T Outcome of a workshop on archiving structural models of biological macromolecules %B Structure %V 14 %N 8 %P 1211-1217 %8 Aug %! Outcome of a workshop on archiving structural models of biological macromolecules %M 16955948 %L 169 %F 169 %K Genomics/trends *Information Systems *Models, Molecular Molecular Biology/*methods/trends Proteins/*chemistry Proteomics/*methods/trends %Z 0969-2126 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. %U http://salilab.org/pdf/Berman_Structure_2006.pdf %+ The Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA. berman@rcsb.rutgers.edu %0 Journal Article %A Berman, HM %A Adams, PD %A Bonvin, AA %A Burley, SK %A Carragher, B %A Wah Chiu, W %A DiMaio, F %A Ferrin, TE %A Gabanyi, MJ %A Goddard, TD %A Griffin, PR %A Haas, J %A Hanke, CA %A Hoch, JC %A Hummer, G %A Kurisu, G %A Lawson, CL %A Leitner, A %A Markley, JL %A Meiler, J %A Montelione, GT %A Phillips, Jr., GN %A Prisner, T %A Rappsilber, J %A Schriemer, DC %A Schwede, T %A Seidel, CAM %A Strutzenberg, TS %A Svergun, DI %A Tajkhorshid, E %A Trewhella, J %A Vallat, B %A Velankar, S %A Vuister, GW %A Webb, B %A Westbrook, JD %A White, KL %A Sali, A %D 2019 %T Federating Structural Models And Data: Outcomes From A Workshop On Archiving Integrative Structures %B Structure %V 27 %N 12 %P 1745-1759 %! Federating Structural Models And Data: Outcomes From A Workshop On Archiving Integrative Structures %R 10.1016/j.str.2019.11.002 %2 PMCID7108332 %M 31780431 %L 398 %F 398 %U https://salilab.org/pdf/Berman_Structure_2019.pdf %0 Journal Article %A Blundell, T. L. %A Cooper, J. B. %A Sali, A. %A Zhu, Z. Y. %D 1991 %T Comparisons of the sequences, 3-D structures and mechanisms of pepsin-like and retroviral aspartic proteinases %B Adv Exp Med Biol %V 306 %P 443-453 %! Comparisons of the sequences, 3-D structures and mechanisms of pepsin-like and retroviral aspartic proteinases %M 1812741 %L 20 %F 20 %K Amino Acid Sequence Aspartic Endopeptidases/antagonists & inhibitors/*chemistry/genetics Binding Sites Comparative Study Models, Molecular Molecular Sequence Data Molecular Structure Pepsin A/antagonists & inhibitors/chemistry/genetics Phylogeny Protein Conformation Research Support, Non-U.S. Gov't Retroviridae/enzymology/genetics Sequence Alignment %Z 0065-2598 Journal Article %+ Imperial Cancer Research Fund Unit of Structural Molecular Biology, Birkbeck College, University of London, United Kingdom. %0 Journal Article %A Blundell, T. L. %A Elliott, G. %A Gardner, S. P. %A Hubbard, T. %A Islam, S. %A Johnson, M. %A Mantafounis, D. %A Murrayrust, P. %A Overington, J. %A Pitts, J. E. %A Sali, A. %A Sibanda, B. L. %A Singh, J. %A Sternberg, M. J. E. %A Sutcliffe, M. J. %A Thornton, J. M. %A Travers, P. %D 1989 %T Protein engineering and design %B Philosophical Transactions of the Royal Society of London Series B-Biological Sciences %V 324 %N 1224 %P 447-460 %! Protein engineering and design %@ 0962-8436 %M WOS:A1989AP05600002 %L 9 %F 9 %U http://salilab.org/pdf/Blundell_PhilTransRSL_1989.pdf %0 Book Section %A Blundell, T.L. %A Carney, D. %A Hubbard, T. %A Johnson, M.S. %A McLeod, A. %A Overington, J.P. %A Sali, A. %A Sutcliffe, M.S. %A Thomas, P. %D 1989 %T Knowledge-based protein modelling and design %E Bloecker, H. %E Collins, J. %E Schmid, R. D. %E Schomburg, D. %B Advances in Protein Design: International Workshop 1988 GBF Monographs %I VCH %V 12 %P 39-43 %! Knowledge-based protein modelling and design %L 10 %F 10 %Z TY - CHAP %U http://salilab.org/pdf/Blundell_AdvInProtSci_1989.pdf %+ Braunschweig %0 Book Section %A Blundell, T.L. %A Cooper, J.B. %A Donnelly, D. %A Driessen, H. %A Edwards, Y. %A Eisenmenger, F. %A Frazao, C. %A Johnson, M. %A Niefind, K. %A Newman, M. %A Overington, J. %A Sali, A. %A Slingsby, C. %A Nalini, V. %A Zhu, Z.Y. %D 1991 %T Patterns of sequence variation in families of homologous proteins %E Jornval, H. %E Hoog, J. O. %E Gustavsson, A. M. %B Methods in Protein Sequence Analysis %C Basel, Switzerland %I Birkhauser Verlag %P 373-385 %! Patterns of sequence variation in families of homologous proteins %L 21 %F 21 %K Proteins methods Sequence Analysis analysis %Z TY - CHAP %U http://salilab.org/pdf/Blundell_MetProtSeqAna_1991.pdf %+ Basel %0 Book Section %A Blundell, T.L. %A Johnson, M.S. %A Overington, J.P. %A Sali, A. %D 1990 %T Knowledge-based protein modeling and the design of novel molecules %E Hook, J. B. %E Poste, G. %B Protein design and the development of new therapeutics and vaccines %C New York, NY %I Plenum Press %P 209-227 %! Knowledge-based protein modeling and the design of novel molecules %L 19 %F 19 %Z TY - CHAP %U http://salilab.org/pdf/Blundell_ProtDesDevTheraVacc_1990.pdf %+ New York, US %0 Journal Article %A Bonanno, J. B. %A Edo, C. %A Eswar, N. %A Pieper, U. %A Romanowski, M. J. %A Ilyin, V. %A Gerchman, S. E. %A Kycia, H. %A Studier, F. W. %A Sali, A. %A Burley, S. K. %D 2001 %T Structural genomics of enzymes involved in sterol/isoprenoid biosynthesis %B Proc Natl Acad Sci U S A %V 98 %N 23 %P 12896-12901 %! Structural genomics of enzymes involved in sterol/isoprenoid biosynthesis %@ 0027-8424 %M 11698677 %L 97 %F 97 %X X-ray structures of two enzymes in the sterol/isoprenoid blosynthesis pathway have been determined in a structural genomics pilot study. Mevalonate-5-diphosphate clecarboxylase (MDD) is a single-domain alpha/beta protein that catalyzes the last of three sequential ATP-depenclent reactions which convert mevalonate to isopentenyl diphosphate. Isopentenyl disphosphate isomerase (IDI) is an alpha/beta metalloenzyme that catalyzes interconversion of isopentenyl diphosphate and dimethylallyl diphosphate, which condense in the next step toward synthesis of sterols and a host of natural products. Homology modeling of related proteins and comparisons of the MIDD and IDI structures with two other experimentally determined structures have shown that MDD is a member of the GHMP superfamily of small-molecule kinases and IDI is similar to the nudix hydrolases, which act on nucleotide diphosphate-containing substrates. Structural models were produced for 379 proteins, encompassing a substantial fraction of both protein superfamilies. All three enzymes responsible for synthesis of isopentenyl diphosphate from mevalonate (mevalonate kinase, phosphomevalonate kinase, and MDD) share the same fold, catalyze phosphorylation of chemically similar substrates (MDD decarboxylation involves phosphorylation of mevalonate diphosphate), and seem to have evolved from a common ancestor. These structures and the structural models derived from them provide a framework for interpreting biochemical function and evolutionary relationships. %U http://salilab.org/pdf/Bonanno_ProcNatlAcadSciUSA_2001.pdf %0 Journal Article %A Bonanno, JB %A Almo, SC %A Bresnick, A %A Chance, MR %A Fiser, A %A Swaminathan, S %A Jiang, J %A Studier, FW %A Shapiro, L %A Lima, CD %A Gaasterland, TM %A Sali, A %A Bain, K %A Feil, I %A Gao, X %A Lorimer, D %A Ramos, A %A Sauder, JM %A Wasserman, SR %A Emtage, S %A D'Amico, KL %A Burley, SK %D 2005 %T New York-Structural GenomiX Research Consortium (NYSGXRC): a large scale center for the protein structure initiative %B J Struct Funct Genom %V 6 %N 2-3 %P 225-232 %! New York-Structural GenomiX Research Consortium (NYSGXRC): a large scale center for the protein structure initiative. %@ 1345-711X %M 16211523 %L 154 %F 154 %K Cloning, Molecular Crystallography, X-Ray Multi-Institutional Systems New York City Nuclear Magnetic Resonance, Biomolecular Proteins Proteomics %X Structural GenomiX, Inc. (SGX), four New York area institutions, and two University of California schools have formed the New York Structural GenomiX Research Consortium (NYSGXRC), an industrial/academic Research Consortium that exploits individual core competencies to support all aspects of the NIH-NIGMS funded Protein Structure Initiative (PSI), including protein family classification and target selection, generation of protein for biophysical analyses, sample preparation for structural studies, structure determination and analyses, and dissemination of results. At the end of the PSI Pilot Study Phase (PSI-1), the NYSGXRC will be capable of producing 100-200 experimentally determined protein structures annually. All Consortium activities can be scaled to increase production capacity significantly during the Production Phase of the PSI (PSI-2). The Consortium utilizes both centralized and de-centralized production teams with clearly defined deliverables and hand-off procedures that are supported by a web-based target/sample tracking system (SGX Laboratory Information Data Management System, LIMS, and NYSGXRC Internal Consortium Experimental Database, ICE-DB). Consortium management is provided by an Executive Committee, which is composed of the PI and all Co-PIs. Progress to date is tracked on a publicly available Consortium web site (http://www.nysgxrc.org) and all DNA/protein reagents and experimental protocols are distributed freely from the New York City Area institutions. In addition to meeting the requirements of the Pilot Study Phase and preparing for the Production Phase of the PSI, the NYSGXRC aims to develop modular technologies that are transferable to structural biology laboratories in both academe and industry. The NYSGXRC PI and Co-PIs intend the PSI to have a transforming effect on the disciplines of X-ray crystallography and NMR spectroscopy of biological macromolecules. Working with other PSI-funded Centers, the NYSGXRC seeks to create the structural biology laboratory of the future. Herein, we present an overview of the organization of the NYSGXRC and describe progress toward development of a high-throughput Gene-->Structure platform. An analysis of current and projected consortium metrics reflects progress to date and delineates opportunities for further technology development. %U http://salilab.org/pdf/Bonanno_JStructFunctGenom_2005.pdf %+ Structural GenomiX, Inc., 10505 Roselle Street, San Diego, CA 92121, USA. %G eng %0 Journal Article %A Bonomi, M. %A Muller, E.G. %A Pellarin, R. %A Kim, S.J. %A Russel, D. %A Ramsden, R. %A Sundin, B. A. %A Davis, T. A. %A Sali, A. %D 2014 %T Determining protein complex structures based on a Bayesian model of in vivo FRET data %B Mol Cell Proteomics %V 13 %P 2812-2823 %! Determining protein complex structures based on a Bayesian model of in vivo FRET data %2 PMCID4223474 %M 25139910;PMCID:PMC4223474 %L 316 %F 316 %U http://salilab.org/pdf/Bonomi_MolCellProteomics_2014.pdf %0 Journal Article %A Bonomi, M %A Hanot, S %A Greenberg, CH %A Sali, A %A Nilges, M %A Vendruscolo, M %A Pellarin, R %D 2018 %T Bayesian weighing of electron cryo-microscopy data for integrative structural modeling %B Structure %P pii: S0969-2126(18)30337-X %! Bayesian weighing of electron cryo-microscopy data for integrative structural modeling %R 10.1101/113951 %2 PMCID6779587 %M 30393052 %L 389 %F 389 %U https://salilab.org/pdf/Bonomi_Structure_2018.pdf %0 Journal Article %A Booth, CR %A Meyer, AS %A Cong, Y %A Topf, M %A Sali, A %A Ludtke, SJ %A Chiu, W %A Frydman, J %D 2008 %T Mechanism of lid closure in the eukaryotic chaperonin TRiC/CCT %B Nat Struct Mol Biol %V 15 %N 7 %P 746-753 %8 Jul %! Mechanism of lid closure in the eukaryotic chaperonin TRiC/CCT. %@ 1545-9985 %2 PMCID2546500 %M 18536725;PMCID:PMC2546500 %L 195 %F 195 %K Animals Cattle Chaperonins Cryoelectron Microscopy Crystallography, X-Ray GroEL Protein Models, Molecular Protein Structure, Secondary Protein Structure, Tertiary Protein Subunits %X All chaperonins mediate ATP-dependent polypeptide folding by confining substrates within a central chamber. Intriguingly, the eukaryotic chaperonin TRiC (also called CCT) uses a built-in lid to close the chamber, whereas prokaryotic chaperonins use a detachable lid. Here we determine the mechanism of lid closure in TRiC using single-particle cryo-EM and comparative protein modeling. Comparison of TRiC in its open, nucleotide-free, and closed, nucleotide-induced states reveals that the interdomain motions leading to lid closure in TRiC are radically different from those of prokaryotic chaperonins, despite their overall structural similarity. We propose that domain movements in TRiC are coordinated through unique interdomain contacts within each subunit and, further, these contacts are absent in prokaryotic chaperonins. Our findings show how different mechanical switches can evolve from a common structural framework through modification of allosteric networks. %Z PMC2546500 %U http://salilab.org/pdf/Booth_NatureStructuralandMolecularBiology_2008.pdf %+ Graduate Program in Structural and Computational Biology and Molecular Biophysics, One Baylor Plaza, Baylor College of Medicine, Houston, Texas 77030, USA. %G eng %0 Journal Article %A Borodovsky, M. %A Koonin, E. %A Burge, C. %A Fickett, J. %A Logsdon, J. %A Sali, A. %A Stormo, G. %A Zhulin, I. %D 2001 %T The third Georgia Tech - Emory international conference on bioinformatics: in silico biology; bioinformatics after human genome November 15-18, 2001, Atlanta, Georgia, USA %B Bioinformatics %V 17 %P 859-861 %8 2001/// %! The third Georgia Tech - Emory international conference on bioinformatics: in silico biology; bioinformatics after human genome November 15-18, 2001, Atlanta, Georgia, USA %M 11673229 %L 101 %F 101 %K Biology Human Genome %Z TY - JOUR %U http://salilab.org/pdf/Borodovsky_Bioinformatics_2001.pdf %0 Journal Article %A Braberg, H. %A Echeverria, I. %A Kaake, R. %A Sali, A. %A Krogan, N. %D 2022 %T From systems to structure — using genetic data to model protein structures %B Nat Rev Genet %V 23 %N 6 %P 342-354 %! From systems to structure — using genetic data to model protein structures %R 10.1038/s41576-021-00441-w %2 PMCID8744059 %M 35013567 %L 428 %F 428 %U https://salilab.org/pdf/Braberg_NatRevGenet_2022.pdf %0 Journal Article %A Braberg, H. %A Webb, B. %A Tjioe, E. %A Pieper, U. %A Sali, A. %A Madhusudhan, M.S. %D 2012 %T SALIGN: A webserver for alignment of multiple protein sequences and structures %B Bioinformatics %V 15 %P 2072-2073 %! SALIGN: A webserver for alignment of multiple protein sequences and structures %2 PMCID3400954 %M 22618536;PMCID:PMC3400954 %L 280 %F 280 %U http://salilab.org/pdf/Braberg_Bioinformatics_2012.pdf %0 Journal Article %A Braberg, H %A Echeverria, I %A Bohn, S %A Cimermancic, P %A Shiver, A %A Alexander, R %A Xu, J %A Shales, M %A Dronamraju, R %A Jiang, S %A Dwivedi, G %A Bogdanoff, D %A Chaung, KK %A Hüttenhain, R %A Wang, S %A Mavor %A D %A Pellarin, R %A Schneidman, D %A Bader, JS %A Fraser, JS %A Morris, J %A Haber, JE %A Strahl, BD %A Gross, CA %A Dai, J %A Boeke, JD %A Sali, A %A Krogan, NJ %D 2020 %T Genetic interaction mapping informs integrative structure determination of protein complexes %B Science %V 370 %N 6522 %! Genetic interaction mapping informs integrative structure determination of protein complexes %R 10.1126/science.aaz4910 %2 PMCID7946025 %M 33303586 %L 406 %F 406 %U https://salilab.org/pdf/Braberg_Science_2020.pdf %0 Journal Article %A Brilot, A. %A Lyon, A. %A Zelter, A. %A Viswanath, S. %A Maxwell, A. %A Yabut, K.C.B. %A MacCoss, M. %A Eric Muller, E. %A Sali, A. %A Davis, T.N. %A Agard, D.A. %D 2021 %T CM1-driven assembly and activation of Yeast γ-Tubulin Small Complex underlies microtubule nucleation %B eLife %V 10 %P e65168 %! CM1-driven assembly and activation of Yeast γ-Tubulin Small Complex underlies microtubule nucleation %R 10.7554/eLife.65168 %2 PMCID8099430 %M 33949948 %L 416 %F 416 %U https://salilab.org/pdf/Brilot_eLife_2021.pdf %0 Journal Article %A Burley, S. K. %A Almo, S. C. %A Bonanno, J. B. %A Capel, M. %A Chance, M. R. %A Gaasterland, T. %A Lin, D. %A Sali, A. %A Studier, F. W. %A Swaminathan, S. %D 1999 %T Structural genomics: beyond the human genome project %B Nat Genet %V 23 %N 2 %P 151-157 %8 Oct %! Structural genomics: beyond the human genome project %M 10508510 %L 75 %F 75 %K Computational Biology/*trends Crystallography, X-Ray Human Genome Project Humans *Protein Conformation Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. %X With access to whole genome sequences for various organisms and imminent completion of the Human Genome Project, the entire process of discovery in molecular and cellular biology is poised to change. Massively parallel measurement strategies promise to revolutionize how we study and ultimately understand the complex biochemical circuitry responsible for controlling normal development, physiologic homeostasis and disease processes. This information explosion is also providing the foundation for an important new initiative in structural biology. We are about to embark on a program of high-throughput X-ray crystallography aimed at developing a comprehensive mechanistic understanding of normal and abnormal human and microbial physiology at the molecular level. We present the rationale for creation of a structural genomics initiative, recount the efforts of ongoing structural genomics pilot studies, and detail the lofty goals, technical challenges and pitfalls facing structural biologists. %Z 1061-4036 Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Burley_NatGenet_1999.pdf %+ Howard Hughes Medical Institute, 1230 York Avenue, New York, New York 10021, USA. burley@rockvax.rockefeller.edu %0 Journal Article %A Burley, S. K. %A Bhikadiya, C. %A Bi, C. %A Bittrich, S. %A Chao, H. %A Chen, L. %A Craig, P. A. %A Crichlow, G. V. %A Dalenberg, K. %A Duarte, J. M. %A Dutta, S. %A Fayazi, M. %A Feng, Z. %A Flatt, J. W. %A Ganesan, S. %A Ghosh, S. %A Goodsell, D. S. %A Green, R. K. %A Guranovic, V. %A Henry, J. %A Hudson, B. P. %A Khokhriakov, I. %A Lawson, C. L. %A Liang, Y. %A Lowe, R. %A Peisach, E. %A Persikova, I. %A Piehl, D. W. %A Rose, Y. %A Sali, A. %A Segura, J. %A Sekharan, M. %A Shao, C. %A Vallat, B. %A Voigt, M. %A Webb, B. %A Westbrook, J. D. %A Whetstone, S. %A Young, J. Y. %A Zalevsky, A. %A Zardecki, C. %D 2023 %T RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning %B Nucleic Acids Res %V 51 %N D1 %P D488-D508 %8 Jan 06 %! RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning %@ 1362-4962 %R 10.1093/nar/gkac1077 %2 PMCID9825554 %M 36420884 %K Artificial Intelligence Databases, Protein Machine Learning Protein Conformation Proteins Reproducibility of Results %X The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities. %Z Burley, Stephen K Bhikadiya, Charmi Bi, Chunxiao Bittrich, Sebastian Chao, Henry Chen, Li Craig, Paul A Crichlow, Gregg V Dalenberg, Kenneth Duarte, Jose M Dutta, Shuchismita Fayazi, Maryam Feng, Zukang Flatt, Justin W Ganesan, Sai Ghosh, Sutapa Goodsell, David S Green, Rachel Kramer Guranovic, Vladimir Henry, Jeremy Hudson, Brian P Khokhriakov, Igor Lawson, Catherine L Liang, Yuhe Lowe, Robert Peisach, Ezra Persikova, Irina Piehl, Dennis W Rose, Yana Sali, Andrej Segura, Joan Sekharan, Monica Shao, Chenghua Vallat, Brinda Voigt, Maria Webb, Ben Westbrook, John D Whetstone, Shamara Young, Jasmine Y Zalevsky, Arthur Zardecki, Christine 2022/11/25 %U https://www.ncbi.nlm.nih.gov/pubmed/36420884 %+ Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA. Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA. Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA. Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA. School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA. Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA. Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. %G eng %0 Journal Article %A Burley, S. K. %A Bhikadiya, C. %A Bi, C. %A Bittrich, S. %A Chen, L. %A Crichlow, G. V. %A Duarte, J. M. %A Dutta, S. %A Fayazi, M. %A Feng, Z. %A Flatt, J. W. %A Ganesan, S. J. %A Goodsell, D. S. %A Ghosh, S. %A Green, R. K. %A Guranovic, V. %A Henry, J. %A Hudson, B. P. %A Lawson, C. L. %A Liang, Y. %A Lowe, R. %A Peisach, E. %A Persikova, I. %A Piehl, D. W. %A Rose, Y. %A Sali, A. %A Segura, J. %A Sekharan, M. %A Shao, C. %A Vallat, B. %A Voigt, M. %A Westbrook, J. D. %A Whetstone, S. %A Young, J. Y. %A Zardecki, C. %D 2022 %T RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D %B Prot Sci %V 31 %N 1 %P 187-208 %! RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D %R 10.1002/pro.4213 %2 PMCID8740825 %M 34676613 %L 426 %F 426 %U https://salilab.org/pdf/Burley_ProtSci_2022.pdf %0 Journal Article %A Burley, S. K. %A Bhikadiya, C. %A Bi, C. %A Bittrich, S. %A Chen, L. %A Crichlow, G. %A Christie, C. H. %A Dalenberg, K. %A Di Costanzo, L. %A Duarte, J. M. %A Dutta, S. %A Feng, Z. %A Ganesan, S. %A Goodsell, D. S. %A Ghosh, S. %A Green, R. K. %A Guranović, V. %A Guzenko, D. %A Hudson, B. P. %A Lawson, C. L. %A Liang, Y. %A Lowe, R. %A Namkoong, H. %A Peisach, E. %A Persikova, I. %A Randle, C. %A Rose, A. %A Rose, Y. %A Sali, A. %A Segura, J. %A Sekharan, M. %A Shao, C. %A Tao, Y. %A Voigt, M. %A Westbrook, J. D. %A Young, J. Y. %A Zardecki, C. %A Zhuravleva, M. %D 2021 %T RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering, and energy sciences %B Nucleic Acids Res %V 49 %N D1 %P D437-D451 %! RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering, and energy sciences %R 10.1093/nar/gkaa1038 %2 PMCID7779003 %M 33211854 %L 411 %F 411 %U https://salilab.org/pdf/Burley_NucleicAcidsRes_2021.pdf %0 Journal Article %A Burley, S. K. %A Kurisu, G. %A Markley, J. L. %A Nakamura, H. %A Velankar, S. %A Berman, H. M. %A Sali, A. %A Schwede, T. %A Trewhella, J. %D 2017 %T PDB-Dev: A Prototype System for Depositing Integrative/Hybrid Structural Models %B Structure %V 25 %N 9 %P 1317-1318 %! PDB-Dev: A Prototype System for Depositing Integrative/Hybrid Structural Models %R 10.1016/j.str.2017.08.001 %2 PMCID5821105 %M 28877501 %L 374 %F 374 %U https://salilab.org/pdf/Burley_Structure_2017.pdf %0 Book Section %A Burley, S.K. %A Almo, S.C. %A Bonanno, J.B. %A Chance, M.R. %A Emtage, S. %A Fiser, A. %A Sali, A. %A Sauder, J.M. %A Swaminathan, S. %D 2008 %T Structure Genomics of Protein Superfamilies %E Gu, J. %E Bourne, P. E. %B Structural Bioinformatics, 2nd Edition %C New York, NY %I Wiley-Blackwell %7 2nd Edition %! Structure Genomics of Protein Superfamilies %L 196 %F 196 %0 Journal Article %A Burley, S.K. %A Berman, H.M. %A Chiu, W. %A Dai, W. %A Flatt, J.W. %A Hudson, B.P. %A Kaelber, J.T. %A Khare, S.D. %A Kulczyk, A.W. %A Lawson, C.L. %A Pintilie, G.D. %A Sali, A. %A Vallat, B. %A Westbrook, J.D. %A Young, J.Y. %A Zardecki, C. %D 2022 %T Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future %B Biophys Rev %V 14 %P 1281-1301 %! Electron microscopy holdings of the Protein Data Bank: the impact of the resolution revolution, new validation tools, and implications for the future %R 10.1007/s12551-022-01013-w %2 PMCID9715422 %M 36474933 %L 438 %F 438 %U https://salilab.org/pdf/Burley_BiophysRev_2022.pdf %0 Journal Article %A Burley, S.K. %A Berman, H.M. %A Duarte, J.M. %A Feng, Z. %A Flatt, J.W. %A Hudson, B.P. %A Lowe, R. %A Peisach, E. %A Piehl, D.W. %A Rose, Y. %A Sali, A. %A Sekharan, M. %A Shao, C. %A Vallat, B. %A Voigt, M. %A Westbrook, J.D. %A Young, J. %A Zardecki, C. %D 2022 %T Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students %B Biomolecules %V 12 %N 10 %P 1425 %! Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students %R 10.3390/biom12101425 %2 PMCID9599165 %M 36291635 %L 437 %F 437 %U https://salilab.org/pdf/Burley_Biomolecules_2022.pdf %0 Journal Article %A Burley, S. %A Bhikadiya, C. %A Bi, C. %A Bittrich, S. %A Chao, H. %A Chen, L. %A Craig, P. %A Crichlow, G. %A Dalenberg, K. %A Duarte, J.M. %A Dutta, S. %A Fayazi, M. %A Feng, Z. %A Flatt, J. %A Ganesan, S. %A Ghosh, S. %A Goodsell, D. %A Green, R.K. %A Guranovic, V. %A Henry, J. %A Hudson, B. %A Lawson, C. %A Liang, Y. %A Lowe, R. %A Peisach, E. %A Persikova, I. %A Piehl, D. %A Rose, Y. %A Sali, A. %A Segura, J. %A Sekharan, M. %A Shao, C. %A Vallat, B. %A Voigt, M. %A Webb, B. %A Westbrook, J. %A Whetstone, S. %A Young, J. %A Zalevsky, A. %A Zardecki, C. %D 2022 %T RCSB Protein Data Bank: Tools for visualizing and understanding biological macromolecules in 3D %B Prot Sci %V 31 %N 12 %P e4482 %! RCSB Protein Data Bank: Tools for visualizing and understanding biological macromolecules in 3D %R 10.1002/pro.4482 %2 PMCID9667899 %M 36281733 %L 435 %F 435 %U https://salilab.org/pdf/Burley_ProtSci_2022a.pdf %0 Journal Article %A Burns, K.H. %A Smail, B. %A Jiang, H. %A Zalevsky, A. %A Dai, N. %A Trachman, R.J. %A Guan, S. %A Sali, A. %A Taylor, M.S. %A LaCava, J. %D 2023 %T Robust cleavage of mismatched DNA by the LINE-1 endonuclease %B J Biol Chem, in press %! Robust cleavage of mismatched DNA by the LINE-1 endonuclease %L 443 %F 443 %0 Journal Article %A Calhoun, S. %A Korczynska, M. %A Wichelecki, D.J. %A San Francisco, B. %A Zhao, S. %A Rodionov, D.A. %A Vetting, M.W. %A Al-Obaidi, N.F. %A Lin, H. %A O’Meara, M.J. %A Scott, D.A. %A Morris, J.H. %A Russel, D. %A Almo, S.C. %A Osterman, A.L. %A Gerlt, J.A. %A Jacobson, M.P. %A Shoichet, B.K. %A Sali, A. %D 2018 %T Prediction of enzymatic pathways by integrative pathway mapping %B eLife %V 7 %P e31097 %! Prediction of enzymatic pathways by integrative pathway mapping %R 10.7554/eLife.31097 %2 PMCID5788505 %M 29377793 %L 382 %F 382 %U https://salilab.org/pdf/Calhoun_eLife_2018.pdf %0 Journal Article %A Carlsson, J. %A Coleman, R. %A Setola, V. %A Irwin, J. %A Fan, H. %A Schlessinger, A. %A Sali, A. %A Roth, B. %A Shoichet, B. %D 2011 %T Structure-based Ligand Discovery Against a Homology Model and X-ray Structure of the Dopamine D3 Receptor %B Nat Chem Biol %V 7 %P 769-778 %! Structure-based Ligand Discovery Against a Homology Model and X-ray Structure of the Dopamine D3 Receptor %2 PMCID3197762 %M 21926995;PMCID:PMC3197762 %L 261 %F 261 %U http://salilab.org/pdf/Carlsson_NatChemBiol_2011.pdf %0 Journal Article %A Carter, L. %A Kim, S.J. %A Schneidman-Duhovny, D. %A Stoehr, J. %A Poncet-Montange, G. %A Weiss, T. %A Tsuruta, H. %A Prusiner, S. %A Sali, A. %D 2015 %T Prion protein-antibody complexes characterized by chromatography-coupled small-angle X-ray scattering %B Biophys J %V 109 %P 793-805 %! Prion protein-antibody complexes characterized by chromatography-coupled small-angle X-ray scattering %R 10.1016/j.bpj.2015.06.065 %2 PMCID4547163 %M 26287631;PMCID:PMC4547163 %L 337 %F 337 %U http://salilab.org/pdf/Carter_BiophysJ_2015.pdf %0 Journal Article %A Carvalho, M. A. %A Marsillac, S. M. %A Karchin, R. %A Manoukian, S. %A Grist, S. %A Swaby, R. F. %A Urmenyi, T. P. %A Rondinelli, E. %A Silva, R. %A Gayol, L. %A Baumbach, L. %A Sutphen, R. %A Pickard-Brzosowicz, J. L. %A Nathanson, K. L. %A Sali, A. %A Goldgar, D. %A Couch, F. J. %A Radice, P. %A Monteiro, A. N. %D 2007 %T Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis %B Cancer Res %V 67 %N 4 %P 1494-1501 %8 Feb 15 %! Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis %M 17308087 %L 176 %F 176 %K Animals BRCA1 Protein/*genetics/*physiology *Genes, BRCA1 Genetic Predisposition to Disease *Germ-Line Mutation Humans *Mutation, Missense Structure-Activity Relationship Variation (Genetics) %X Germ line inactivating mutations in BRCA1 confer susceptibility for breast and ovarian cancer. However, the relevance of the many missense changes in the gene for which the effect on protein function is unknown remains unclear. Determination of which variants are causally associated with cancer is important for assessment of individual risk. We used a functional assay that measures the transactivation activity of BRCA1 in combination with analysis of protein modeling based on the structure of BRCA1 BRCT domains. In addition, the information generated was interpreted in light of genetic data. We determined the predicted cancer association of 22 BRCA1 variants and verified that the common polymorphism S1613G has no effect on BRCA1 function, even when combined with other rare variants. We estimated the specificity and sensitivity of the assay, and by meta-analysis of 47 variants, we show that variants with <45% of wild-type activity can be classified as deleterious whereas variants with >50% can be classified as neutral. In conclusion, we did functional and structure-based analyses on a large series of BRCA1 missense variants and defined a tentative threshold activity for the classification missense variants. By interpreting the validated functional data in light of additional clinical and structural evidence, we conclude that it is possible to classify all missense variants in the BRCA1 COOH-terminal region. These results bring functional assays for BRCA1 closer to clinical applicability. %Z 0008-5472 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Carvalho_CancerRes_2007.pdf %+ Risk Assessment, Detection, and Intervention Program, H. Lee Moffitt Cancer Center, University of South Florida College of Medicine, 12902 Magnolia Drive, Tampa, FL 33612, USA. %0 Journal Article %A Chance, M. R. %A Bresnick, A. R. %A Burley, S. K. %A Jiang, J. S. %A Lima, C. D. %A Sali, A. %A Almo, S. C. %A Bonanno, J. B. %A Buglino, J. A. %A Boulton, S. %A Chen, H. %A Eswar, N. %A He, G. S. %A Huang, R. %A Ilyin, V. %A McMahan, L. %A Pieper, U. %A Ray, S. %A Vidal, M. %A Wang, L. K. %D 2002 %T Structural genomics: A pipeline for providing structures for the biologist %B Protein Sci %V 11 %N 4 %P 723-738 %! Structural genomics: A pipeline for providing structures for the biologist %@ 0961-8368 %R 10.1110/ps.470102 %M 11910018 %L 111 %F 111 %U http://salilab.org/pdf/Chance_ProteinSci_2002.pdf %0 Journal Article %A Chance, M. R. %A Fiser, A. %A Sali, A. %A Pieper, U. %A Eswar, N. %A Xu, G. %A Fajardo, J. E. %A Radhakannan, T. %A Marinkovic, N. %D 2004 %T High-throughput computational and experimental techniques in structural genomics %B Genome Res %V 14 %N 10B %P 2145-2154 %8 Oct %! High-throughput computational and experimental techniques in structural genomics %M 15489337 %L 137 %F 137 %K Algorithms Animals Computational Biology Databases, Protein Decision Trees *Genomics Humans Protein Sorting Signals Proteins/*chemistry/*genetics Proteomics Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Analysis, Protein *Structural Homology, Protein %X Structural genomics has as its goal the provision of structural information for all possible ORF sequences through a combination of experimental and computational approaches. The access to genome sequences and cloning resources from an ever-widening array of organisms is driving high-throughput structural studies by the New York Structural Genomics Research Consortium. In this report, we outline the progress of the Consortium in establishing its pipeline for structural genomics, and some of the experimental and bioinformatics efforts leading to structural annotation of proteins. The Consortium has established a pipeline for structural biology studies, automated modeling of ORF sequences using solved (template) structures, and a novel high-throughput approach (metallomics) to examining the metal binding to purified protein targets. The Consortium has so far produced 493 purified proteins from >1077 expression vectors. A total of 95 have resulted in crystal structures, and 81 are deposited in the Protein Data Bank (PDB). Comparative modeling of these structures has generated >40,000 structural models. We also initiated a high-throughput metal analysis of the purified proteins; this has determined that 10%-15% of the targets contain a stoichiometric structural or catalytic transition metal atom. The progress of the structural genomics centers in the U.S. and around the world suggests that the goal of providing useful structural information on most all ORF domains will be realized. This projected resource will provide structural biology information important to understanding the function of most proteins of the cell. %Z 1088-9051 Journal Article %U http://salilab.org/pdf/Chance_GenomeRes_2004.pdf %+ New York Structural Genomics Research Consortium, Albert Einstein College of Medicine, Bronx, New York 10461, USA. mrc@aecom.yu.edu %0 Journal Article %A Chandramouli, P %A Topf, M %A Menetret, JF %A Eswar, N %A Cannone, JJ %A Gutell, RR %A Sali, A %A Akey, CW %D 2008 %T Structure of the mammalian 80S ribosome at 8.7 A resolution %B Structure %V 16 %N 4 %P 535-548 %8 Apr %! Structure of the mammalian 80S ribosome at 8.7 A resolution. %@ 0969-2126 %2 PMCID2775484 %M 18400176 %L 198 %F 198 %K Animals Cryoelectron Microscopy Dogs Image Processing, Computer-Assisted Models, Molecular Protein Biosynthesis RNA, Ribosomal RNA, Transfer Receptors, Cell Surface Ribosomal Proteins Ribosome Subunits, Small, Eukaryotic Ribosomes %X In this paper, we present a structure of the mammalian ribosome determined at approximately 8.7 A resolution by electron cryomicroscopy and single-particle methods. A model of the ribosome was created by docking homology models of subunit rRNAs and conserved proteins into the density map. We then modeled expansion segments in the subunit rRNAs and found unclaimed density for approximately 20 proteins. In general, many conserved proteins and novel proteins interact with expansion segments to form an integrated framework that may stabilize the mature ribosome. Our structure provides a snapshot of the mammalian ribosome at the beginning of translation and lends support to current models in which large movements of the small subunit and L1 stalk occur during tRNA translocation. Finally, details are presented for intersubunit bridges that are specific to the eukaryotic ribosome. We suggest that these bridges may help reset the conformation of the ribosome to prepare for the next cycle of chain elongation. %U http://salilab.org/pdf/Chandramouli_Structure_2008.pdf %+ Department of Physiology and Biophysics, Boston University School of Medicine, 700 Albany Street, Boston, MA 02118, USA. %G eng %0 Journal Article %A Chen, A. %A Vieth, M. %A Timm, D. %A Humblet, C. %A Schneidman-Duhovny, D. %A Chemmama, I.E. %A Sali, A. %A Lu, J. %A Liu, L. %D 2017 %T Reconstruction of 3D structures of MET Antibodies from Electron Microscopy 2D Class Averages %B PloS One %V 12 %N 4 %P e0175758 %! Reconstruction of 3D structures of MET Antibodies from Electron Microscopy 2D Class Averages %R 10.1371/journal.pone.0175758 %2 PMCID5391116 %M 28406969 %L 367 %F 367 %U https://salilab.org/pdf/Chen_PloSOne_2017.pdf %0 Journal Article %A Chen, EC %A Khuri, N %A Liang, X %A Stecula, A %A Chien, H %A Yee, SW %A Huang, Y %A Sali, A %A Giacomini, KM %D 2017 %T Discovery of competitive and non-competitive ligands of the organic cation transporter 1 (OCT1; SLC22A1) %B J Med Chem %V 60 %N 7 %P 2685-2696 %! Discovery of competitive and non-competitive ligands of the organic cation transporter 1 (OCT1; SLC22A1) %R 10.1021/acs.jmedchem.6b01317 %M 28230985 %L 368 %F 368 %U https://salilab.org/pdf/Chen_JMedChem_2017.pdf %0 Journal Article %A Chen, L. %A Pawlikowski, B. %A Schlessinger, A. %A More, S. %A Stryke, D. %A Johns, S.J. %A Portman, M. %A Ferrin, T.E. %A Sali, A. %A Giacomini, K. %D 2010 %T Role of organic cation transporter 3 (SLC22A3) and Its missense variants in the pharmacologic action of metformin %B Pharmacogenet Genomics %V 20 %P 687-699 %! Role of organic cation transporter 3 (SLC22A3) and Its missense variants in the pharmacologic action of metformin %2 PMCID2976715 %M 20859243;PMCID:PMC2976715 %L 238 %F 238 %U http://salilab.org/pdf/Chen_PharmacogenetGenomics_2010a.pdf %0 Journal Article %A Chen, L. %A Takizawa, M. %A Chen, E. %A Schlessinger, A. %A Choi, J.H. %A Segenthelar, J. %A Sali, A. %A Kubo, M. %A Nakamura, S. %A Iwamoto, Y. %A Iwasaki, N. %A Giacomini, K.M. %D 2010 %T Genetic polymorphisms in the organic cation transporter 1, OCT1, in Chinese and Japanese populations, exhibit altered function %B J Pharmacol Exp Ther %V 1 %P 42-50 %! Genetic polymorphisms in the organic cation transporter 1, OCT1, in Chinese and Japanese populations, exhibit altered function %2 PMCID2957788 %M 20639304;PMCID:PMC2957788 %L 245 %F 245 %U http://salilab.org/pdf/Chen_JPharmacolExpTher_2010.pdf %0 Journal Article %A Chen, Z. A. %A Pellarin, R. %A Fischer, L. %A Sali, A. %A Nilges, M. %A Barlow, P. N. %A Rappsilber, J. %D 2016 %T Structure of complement C3(H2O) revealed by quantitative cross-linking/mass spectrometry and modelling %B Mol Cell Proteomics %V 15 %N 6 %P 2730-2743 %! Structure of complement C3(H2O) revealed by quantitative cross-linking/mass spectrometry and modelling %R 10.1101/056457 %2 PMCID4974347 %M 27250206 %L 353 %F 353 %U https://salilab.org/pdf/Chen_MolCellProteomics_2016.pdf %0 Journal Article %A Chiang, RA %A Sali, A %A Babbitt, PC %D 2008 %T Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies %B PLoS Comput Biol %V 4 %N 8 %P e1000142 %! Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies. %@ 1553-7358 %2 PMCID2453236 %M 18670595;PMCID:PMC2453236 %L 208 %F 208 %K Amino Acid Sequence Animals Catalysis Cluster Analysis Computational Biology Conserved Sequence Databases, Protein Enzymes Evolution, Molecular Humans Pattern Recognition, Automated Protein Binding Protein Interaction Domains and Motifs Sequence Alignment Structural Homology, Protein Structure-Activity Relationship Substrate Specificity %X The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies. %U http://salilab.org/pdf/Chiang_PLoSComputationalBiology_2008.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biosciences, University of California at San Francisco, San Francisco, California, United States of America. %G eng %0 Journal Article %A Choi, J. %A Yee, S. %A Ramirez, A. %A Morrissey, K. %A Jang, G. %A Joski, P. %A Mefford, J. %A Hesselson, S. %A Schlessinger, A. %A Jenkins, G. %A Castro, R. %A Johns, S. %A Stryke, D. %A Sali, A. %A Ferrin, T. %A Witte, J. %A Kwok, P. %A Roden, D. %A Wilke, R. %A McCarty, C. %A Davis, R. %A Giacomini, K. %D 2011 %T A Common Promoter Variant in MATE2-K Is Associated with Poor Response to Metformin %B Clin Pharmacol Ther %V 90 %P 674-684 %! A Common Promoter Variant in MATE2-K Is Associated with Poor Response to Metformin %2 PMCID3329222 %M 21956618;PMCID:PMC3329222 %L 263 %F 263 %U http://salilab.org/pdf/Choi_ClinPharmacolTher_2011.pdf %0 Journal Article %A Choudhury, A. R. %A Sikorska, E. %A van den Boom, J. %A Bayer, P. %A Popenda, L. %A Szutkowski, K. %A Jurga, S. %A Bonomi, M. %A Sali, A. %A Zhukov, I. %A Passamonti, S. %A Novič, M. %D 2015 %T Structural model of the Bilitranslocase transmembrane domain supported by NMR and FRET data %B PloS One %V 10 %N 8 %P e0135455 %! Structural model of the Bilitranslocase transmembrane domain supported by NMR and FRET data %2 PMCID4546402 %M 26291722 %L 343 %F 343 %U http://salilab.org/pdf/Choudhury_PLosOne_2015.pdf %0 Journal Article %A Cimermancic, P. %A Medema, M.H. %A Claesen, J. %A Kurita, K. %A Wieland Brown, L. %A Mavrommatis, K. %A Pati, A. %A Godfrey, P.A. %A Koehrsen, M. %A Clardy, J. %A Birren, B.W. %A Takano, E. %A Sali, A. %A Linington, R.G. %A Fischbach, M. %D 2014 %T Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters %B Cell %V 158 %P 412-421 %! Insights into secondary metabolism from a global analysis of prokaryotic biosynthetic gene clusters %2 PMCID4123684 %M 25036635;PMCID:PMC4123684 %L 327 %F 327 %U http://salilab.org/pdf/Cimermancic_Cell_2014.pdf %0 Journal Article %A Cimermancic, P. %A Weinkam, P. %A Rettenmaier, T. J. %A Bichmann, L. %A Keedy, D. A. %A Woldeyes, R. A. %A Schneidman-Duhovny, D. %A Demerdash, O. N. %A Mitchell, J. C. %A Wells, J. A. %A Fraser, J. S. %A Sali, A. %D 2016 %T CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites. %B J Mol Biol %V 428 %N 4 %P 709-19 %! CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites. %R 10.1016/j.jmb.2016.01.029 %2 PMCID4794384 %M 26854760 %L 346 %F 346 %U https://salilab.org/pdf/Cimermancic_JMolBiol_2016.pdf %W https://github.com/salilab/cryptosite %0 Journal Article %A Clark, T. %A Mohan, J. %A Schaffer, L. %A Obernier, K. %A Al Manir, S. %A Churas, C. P. %A Dailamy, A. %A Doctor, Y. %A Forget, A. %A Hansen, J. N. %A Hu, M. %A Lenkiewicz, J. %A Levinson, M. A. %A Marquez, C. %A Nourreddine, S. %A Niestroy, J. %A Pratt, D. %A Qian, G. %A Thaker, S. %A Bélisle-Pipon, J. %A Brandt, C. %A Chen, J. %A Ding, Y. %A Fodeh, S. %A Krogan, N. %A Lundberg, E. %A Mali, P. %A Payne-Foster, P. %A Ratcliffe, S. %A Ravitsky, V. %A Sali, A. %A Schulz, W. %A Ideker, T. %D 2024 %T Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines %B in press %! Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines %R 10.1101/2024.05.21.589311 %2 PMCID11142054 %M 38826258 %L 457 %F 457 %0 Journal Article %A Colubri, A %A Jha, AK %A Shen, MY %A Sali, A %A Berry, RS %A Sosnick, TR %A Freed, KF %D 2006 %T Minimalist representations and the importance of nearest neighbor effects in protein folding simulations %B J Mol Biol %V 363 %N 4 %P 835-857 %8 Nov %! Minimalist representations and the importance of nearest neighbor effects in protein folding simulations. %@ 0022-2836 %M 16982067 %L 170 %F 170 %K Amino Acids Computer Simulation Databases, Protein Hydrogen Bonding Models, Molecular Protein Folding Protein Structure, Secondary Proteins Thermodynamics %X In order to investigate the level of representation required to simulate folding and predict structure, we test the ability of a variety of reduced representations to identify native states in decoy libraries and to recover the native structure given the advanced knowledge of the very broad native Ramachandran basin assignments. Simplifications include the removal of the entire side-chain or the retention of only the Cbeta atoms. Scoring functions are derived from an all-atom statistical potential that distinguishes between atoms and different residue types. Structures are obtained by minimizing the scoring function with a computationally rapid simulated annealing algorithm. Results are compared for simulations in which backbone conformations are sampled from a Protein Data Bank-based backbone rotamer library generated by either ignoring or including a dependence on the identity and conformation of the neighboring residues. Only when the Cbeta atoms and nearest neighbor effects are included do the lowest energy structures generally fall within 4 A of the native backbone root-mean square deviation (RMSD), despite the initial configuration being highly expanded with an average RMSD > or = 10 A. The side-chains are reinserted into the Cbeta models with minimal steric clash. Therefore, the detailed, all-atom information lost in descending to a Cbeta-level representation is recaptured to a large measure using backbone dihedral angle sampling that includes nearest neighbor effects and an appropriate scoring function. %U http://salilab.org/pdf/Colubri_JMolBiol_2006.pdf %+ Department of Chemistry, The University of Chicago, Chicago, IL 60637, USA. %G eng %0 Journal Article %A Cong, Y %A Topf, M %A Sali, A %A Matsudaira, P %A Dougherty, M %A Chiu, W %A Schmid, MF %D 2008 %T Crystallographic conformers of actin in a biologically active bundle of filaments %B J Mol Biol %V 375 %N 2 %P 331-336 %8 Jan %! Crystallographic conformers of actin in a biologically active bundle of filaments. %@ 1089-8638 %2 PMCID2680129 %M 18022194;PMCID:PMC2680129 %L 194 %F 194 %K Acrosome Reaction Actins Animals Biomechanics Crystallography, X-Ray Horseshoe Crabs Male Microfilaments Models, Molecular Protein Conformation Protein Structure, Quaternary Protein Structure, Tertiary Spermatozoa %X Actin carries out many of its cellular functions through its filamentous form; thus, understanding the detailed structure of actin filaments is an essential step in achieving a mechanistic understanding of actin function. The acrosomal bundle in the Limulus sperm has been shown to be a quasi-crystalline array with an asymmetric unit composed of a filament with 14 actin-scruin pairs. The bundle in its true discharge state penetrates the jelly coat of the egg. Our previous electron crystallographic reconstruction demonstrated that the actin filament cross-linked by scruin in this acrosomal bundle state deviates significantly from a perfect F-actin helix. In that study, the tertiary structure of each of the 14 actin protomers in the asymmetric unit of the bundle filament was assumed to be constant. In the current study, an actin filament atomic model in the acrosomal bundle has been refined by combining rigid-body docking with multiple actin crystal structures from the Protein Data Bank and constrained energy minimization. Our observation demonstrates that actin protomers adopt different tertiary conformations when they form an actin filament in the bundle. The scruin and bundle packing forces appear to influence the tertiary and quaternary conformations of actin in the filament of this biologically active bundle. %U http://salilab.org/pdf/Cong_JMolBiol_2008.pdf %+ National Center for Macromolecular Imaging and Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA. mschmid@bcm.tmc.edu %G eng %0 Journal Article %A Davis, F. P. %A Barkan, D. T. %A Eswar, N. %A McKerrow, J. H. %A Sali, A. %D 2007 %T Host pathogen protein interactions predicted by comparative modeling %B Protein Sci %V 16 %N 12 %P 2585-2596 %8 Dec %! Host pathogen protein interactions predicted by comparative modeling %@ 0961-8368 (Print) %M 17965183 %L 184 %F 184 %K Algorithms Bacterial Proteins/chemistry/*metabolism *Computational Biology Databases, Protein *Host-Pathogen Interactions Humans Mycobacterium/metabolism Protein Binding *Protein Interaction Mapping Proteins/*chemistry/*metabolism Protozoan Proteins/chemistry/*metabolism Sequence Analysis, Protein Software %X Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions. %Z P01-AI35707/AI/United States NIAID R01-GM54762/GM/United States NIGMS U54 RR022220/RR/United States NCRR Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. United States a publication of the Protein Society %U http://salilab.org/pdf/Davis_ProteinSci_2007.pdf %+ Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, California 94158, USA. davisf@janelia.hhmi.org %G eng %0 Journal Article %A Davis, F. P. %A Braberg, H. %A Shen, M. Y. %A Pieper, U. %A Sali, A. %A Madhusudhan, M. S. %D 2006 %T Protein complex compositions predicted by structural similarity %B Nucleic Acids Res %V 34 %N 10 %P 2943-2952 %! Protein complex compositions predicted by structural similarity %M 16738133 %L 167 %F 167 %K Algorithms Computational Biology/methods Models, Molecular Multiprotein Complexes/*chemistry/metabolism Protein Binding Protein Interaction Mapping/*methods Protein Structure, Tertiary ROC Curve Saccharomyces cerevisiae Proteins/*chemistry/metabolism alpha-Amylase/chemistry/metabolism %X Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain-porcine alpha-amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase). %Z 1362-4962 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Validation Studies %U http://salilab.org/pdf/Davis_NucleicAcidsRes_2006.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, University of California San Francisco, 1700 4th Street, Byers Hall, San Francisco, CA 94143-2552, USA. %0 Journal Article %A Davis, F.P. %A Sali, A. %D 2010 %T The overlap of small molecule and protein binding sites within families of protein structures %B PLoS Comp Biol %V 6 %N 2 %P e1000668 %! The overlap of small molecule and protein binding sites within families of protein structures %2 PMCID2816688 %M 20140189;PMCID:PMC2816688 %L 235 %F 235 %U http://salilab.org/pdf/Davis_PLoSCompBiol_2010.pdf %0 Journal Article %A Davis, FP %A Sali, A %D 2005 %T PIBASE: a comprehensive database of structurally defined protein interfaces %B Bioinformatics %V 21 %N 9 %P 1901-1907 %8 May %! PIBASE: a comprehensive database of structurally defined protein interfaces. %@ 1367-4803 %M 15657096 %L 147 %F 147 %K Algorithms Amino Acid Sequence Binding Sites Computer Simulation Databases, Protein Models, Chemical Molecular Sequence Data Protein Binding Protein Interaction Mapping Protein Structure, Tertiary Proteins Sequence Alignment Sequence Analysis, Protein Structure-Activity Relationship %X MOTIVATION: In recent years, the Protein Data Bank (PDB) has experienced rapid growth. To maximize the utility of the high resolution protein-protein interaction data stored in the PDB, we have developed PIBASE, a comprehensive relational database of structurally defined interfaces between pairs of protein domains. It is composed of binary interfaces extracted from structures in the PDB and the Probable Quaternary Structure server using domain assignments from the Structural Classification of Proteins and CATH fold classification systems. RESULTS: PIBASE currently contains 158,915 interacting domain pairs between 105,061 domains from 2125 SCOP families. A diverse set of geometric, physiochemical and topologic properties are calculated for each complex, its domains, interfaces and binding sites. A subset of the interface properties are used to remove interface redundancy within PDB entries, resulting in 20,912 distinct domain-domain interfaces. The complexes are grouped into 989 topological classes based on their patterns of domain-domain contacts. The binary interfaces and their corresponding binding sites are categorized into 18,755 and 30,975 topological classes, respectively, based on the topology of secondary structure elements. The utility of the database is illustrated by outlining several current applications. AVAILABILITY: The database is accessible via the world wide web at http://salilab.org/pibase SUPPLEMENTARY INFORMATION: http://salilab.org/pibase/suppinfo.html. %U http://salilab.org/pdf/Davis_Bioinformatics_2005.pdf %+ Graduate Group in Biophysics, California Institute for Quantitative Biomedical Research, University of California, San Francisco, 94143, USA. %G eng %0 Journal Article %A DeGrasse, J.A. %A DuBois, K.N. %A Devos, D. %A Siegel, T.N. %A Sali, A. %A Field, M.C. %A Rout, M.P. %A Chait, B.T. %D 2009 %T The Establishment of Nuclear Pore Complex Architecture Occurred Early in Evolution %B Mol Cell Proteomics %V 8 %P 2119-2130 %! The Establishment of Nuclear Pore Complex Architecture Occurred Early in Evolution. %2 PMCID2742445 %M 19525551;PMCID:PMC2742445 %L 228 %F 228 %U http://salilab.org/pdf/DeGrasse_MolecularandCellularProteomics_2009.pdf %0 Journal Article %A Devos, D. %A Dokudovskaya, S. %A Alber, F. %A Williams, R. %A Chait, B. T. %A Sali, A. %A Rout, M. P. %D 2004 %T Components of coated vesicles and nuclear pore complexes share a common molecular architecture %B PLoS Biology %V 2 %N 12 %P e380 %8 Dec %! Components of coated vesicles and nuclear pore complexes share a common molecular architecture %M 15523559 %L 140 %F 140 %X Numerous features distinguish prokaryotes from eukaryotes, chief among which are the distinctive internal membrane systems of eukaryotic cells. These membrane systems form elaborate compartments and vesicular trafficking pathways, and sequester the chromatin within the nuclear envelope. The nuclear pore complex is the portal that specifically mediates macromolecular trafficking across the nuclear envelope. Although it is generally understood that these internal membrane systems evolved from specialized invaginations of the prokaryotic plasma membrane, it is not clear how the nuclear pore complex could have evolved from organisms with no analogous transport system. Here we use computational and biochemical methods to perform a structural analysis of the seven proteins comprising the yNup84/vNup107-160 subcomplex, a core building block of the nuclear pore complex. Our analysis indicates that all seven proteins contain either a beta-propeller fold, an alpha-solenoid fold, or a distinctive arrangement of both, revealing close similarities between the structures comprising the yNup84/vNup107-160 subcomplex and those comprising the major types of vesicle coating complexes that maintain vesicular trafficking pathways. These similarities suggest a common evolutionary origin for nuclear pore complexes and coated vesicles in an early membrane-curving module that led to the formation of the internal membrane systems in modern eukaryotes. %Z 1545-7885 Journal Article %U http://salilab.org/pdf/Devos_PLoSBiology_2004.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, University of California, San Francisco, USA. %0 Journal Article %A Devos, D. %A Dokudovskaya, S. %A Williams, R. %A Alber, F. %A Eswar, N. %A Chait, B. T. %A Rout, M. P. %A Sali, A. %D 2006 %T Simple fold composition and modular architecture of the nuclear pore complex %B Proc Natl Acad Sci U S A %V 103 %N 7 %P 2172-2177 %8 Feb 14 %! Simple fold composition and modular architecture of the nuclear pore complex %M 16461911 %L 157 %F 157 %K Computational Biology Evolution, Molecular Karyopherins/chemistry Nuclear Pore/*chemistry Nuclear Pore Complex Proteins/*chemistry Protein Folding Protein Structure, Secondary Protein Structure, Tertiary Saccharomyces cerevisiae/*metabolism Saccharomyces cerevisiae Proteins/*chemistry %X The nuclear pore complex (NPC) consists of multiple copies of approximately 30 different proteins [nucleoporins (nups)], forming a channel in the nuclear envelope that mediates macromolecular transport between the cytosol and the nucleus. With <5% of the nup residues currently available in experimentally determined structures, little is known about the detailed structure of the NPC. Here, we use a combined computational and biochemical approach to assign folds for approximately 95% of the residues in the yeast and vertebrate nups. These fold assignments suggest an underlying simplicity in the composition and modularity in the architecture of all eukaryotic NPCs. The simplicity in NPC composition is reflected in the presence of only eight fold types, with the three most frequent folds accounting for approximately 85% of the residues. The modularity in NPC architecture is reflected in its hierarchical and symmetrical organization that partitions the predicted nup folds into three groups: the transmembrane group containing transmembrane helices and a cadherin fold, the central scaffold group containing beta-propeller and alpha-solenoid folds, and the peripheral FG group containing predominantly the FG repeats and the coiled-coil fold. Moreover, similarities between structures in coated vesicles and those in the NPC support our prior hypothesis for their common evolutionary origin in a progenitor protocoatomer. The small number of predicted fold types in the NPC and their internal symmetries suggest that the bulk of the NPC structure has evolved through extensive motif and gene duplication from a simple precursor set of only a few proteins. %Z 0027-8424 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Devos_ProcNatlAcadSciUSA_2006.pdf %+ Department of Biopharmaceutical Sciences, University of California, Mission Bay QB3, 1700 4th Street, Suite 503B, San Francisco, CA 94143-2552, USA. %0 Journal Article %A Dinner, A. R. %A Sali, A. %A Karplus, M. %D 1996 %T The folding mechanism of larger model proteins: role of native structure %B Proc Natl Acad Sci U S A %V 93 %N 16 %P 8356-8361 %8 Aug 6 %! The folding mechanism of larger model proteins: role of native structure %M 8710875 %L 50 %F 50 %K Models, Theoretical Monte Carlo Method Peptides/*chemistry *Protein Folding Proteins/*chemistry Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Structure-Activity Relationship Thermodynamics %X The folding mechanism of a 125-bead heteropolymer model for proteins is investigated with Monte Carlo simulations on a cubic lattice. Sequences that do and do not fold in a reasonable time are compared. The overall folding behavior is found to be more complex than that of models for smaller proteins. Folding begins with a rapid collapse followed by a slow search through the semi-compact globule for a sequence-dependent stable core with about 30 out of 176 native contacts which serves as the transition state for folding to a near-native structure. Efficient search for the core is dependent on structural features of the native state. Sequences that fold have large amounts of stable, cooperative structure that is accessible through short-range initiation sites, such as those in anti-parallel sheets connected by turns. Before folding is completed, the system can encounter a second bottleneck, involving the condensation and rearrangement of surface residues. Overly stable local structure of the surface residues slows this stage of the folding process. The relation of the results from the 125-mer model studies to the folding of real proteins is discussed. %Z 0027-8424 Journal Article %U http://salilab.org/pdf/Dinner_ProcNatlAcadSciUSA_1996.pdf %+ Committee on Higher Degrees in Biophysics, Department of Chemistry, Harvard University, Cambridge, MA 02138, USA. %0 Journal Article %A Dinner, A. R. %A Sali, A. %A Smith, L. J. %A Dobson, C. M. %A Karplus, M. %D 2000 %T Understanding protein folding via free-energy surfaces from theory and experiment %B Trends Biochem Sci %V 25 %N 7 %P 331-339 %8 Jul %! Understanding protein folding via free-energy surfaces from theory and experiment %M 10871884 %L 82 %F 82 %K Animals Computer Simulation Kinetics Models, Molecular Protein Conformation *Protein Folding Proteins/*chemistry/*metabolism Research Support, Non-U.S. Gov't Temperature Thermodynamics %X The ability of protein molecules to fold into their highly structured functional states is one of the most remarkable evolutionary achievements of biology. In recent years, our understanding of the way in which this complex self-assembly process takes place has increased dramatically. Much of the reason for this advance has been the development of energy surfaces (landscapes), which allow the folding reaction to be described and visualized in a meaningful manner. Analysis of these surfaces, derived from the constructive interplay between theory and experiment, has led to the development of a unified mechanism for folding and a recognition of the underlying factors that control the rates and products of the folding process. %Z 0968-0004 Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Dinner_TrendsBiochemSci_2000.pdf %+ aOxford Centre for Molecular Sciences, New Chemistry Laboratory, University of Oxford, South Parks Road, Oxford, UK OX1 3QT. %0 Journal Article %A Dinner, A. %A Sali, A. %A Karplus, M. %A Shakhnovich, E. %D 1994 %T Phase diagram of a model protein derived by exhaustive enumeration of the conformations %B J Chem Phys %V 101 %N 2 %P 1444-1451 %! Phase diagram of a model protein derived by exhaustive enumeration of the conformations %@ 0021-9606 %L 35 %F 35 %X An understanding of the various states available to a polypeptide chain is important for a description of the protein folding process. We use a 16-monomer chain on a two-dimensional square lattice to model a protein. This makes it possible to enumerate all self-avoiding conformations from which any equilibrium thermodynamic quantity can be calculated. By varying the external conditions of temperature and average attraction, we construct a phase diagram for the model protein. It is found to have an extended coil state, a homopolymer-like disorganized globule state, and an organized frozen globule state that corresponds to the lowest energy (native) conformation. The exact model results agree well with analytical heteropolymer theory. %U http://salilab.org/pdf/Dinner_JChemPhys_1994.pdf %0 Journal Article %A Dobson, C.M. %A Sali, A. %A Karplus, M. %D 1998 %T Protein folding: A perspective from theory and experiment %B Angewandte Chemie Int Ed %V 37 %P 868-893 %8 1998/// %! Protein folding: A perspective from theory and experiment %L 62 %F 62 %K Protein Folding %Z TY - JOUR %U http://salilab.org/pdf/Dobson_AngewandteChemieIntEd_1998.pdf %0 Journal Article %A Dokudovskaya, S. %A Waharte, F. %A Schlessinger, A. %A Pieper, U. %A Devos, D.P. %A Cristea, I.M. %A Williams, R. %A Salamero, J. %A Chait, B.T. %A Sali, A. %A Field, M.C. %A Rout, M.C. %A Dargemont, C. %D 2011 %T A conserved coatomer-related complex containing Sec13 and Seh1 dynamically associates with the vacuole in Saccharomyces cerevisiae %B Mol Cell Proteomics %V 10 %P M110.006478 %! A conserved coatomer-related complex containing Sec13 and Seh1 dynamically associates with the vacuole in Saccharomyces cerevisiae %2 PMCID3108837 %M 21454883;PMCID:PMC3108837 %L 255 %F 255 %U http://salilab.org/pdf/Dokudovskaya_MolCellProteomics_2011.pdf %0 Journal Article %A Dokudovskaya, S. %A Williams, R. %A Devos, D. %A Sali, A. %A Chait, B. T. %A Rout, M. P. %D 2006 %T Protease accessibility laddering: a proteomic tool for probing protein structure %B Structure %V 14 %N 4 %P 653-660 %8 Apr %! Protease accessibility laddering: a proteomic tool for probing protein structure %M 16615907 %L 164 %F 164 %K Adaptor Proteins, Signal Transducing/chemistry Animals Bacterial Proteins/chemistry Clathrin Heavy Chains/chemistry Computational Biology/methods Crystallography, X-Ray Electrophoresis, Polyacrylamide Gel Fungal Proteins/chemistry Genome Genomics/methods Green Fluorescent Proteins/chemistry/metabolism Humans Immunoblotting Ligands Models, Biological Models, Molecular Molecular Conformation Peptide Hydrolases/*chemistry/metabolism Protein Binding Protein Conformation Protein Folding Protein Structure, Tertiary Proteins/chemistry Proteomics/*methods Saccharomyces cerevisiae/metabolism %X Limited proteolysis is widely used in biochemical and crystallographic studies to determine domain organization, folding properties, and ligand binding activities of proteins. The method has limitations, however, due to the difficulties in obtaining sufficient amounts of correctly folded proteins and in interpreting the results of the proteolysis. A new limited proteolysis method, named protease accessibility laddering (PAL), avoids these complications. In PAL, tagged proteins are purified on magnetic beads in their natively folded state. While attached to the beads, proteins are probed with proteases. Proteolytic fragments are eluted and detected by immunoblotting with antibodies against the tag (e.g., Protein A, GFP, and 6xHis). PAL readily detects domain boundaries and flexible loops within proteins. A combination of PAL and comparative protein structure modeling allows characterization of previously unknown structures (e.g., Sec31, a component of the COPII coated vesicle). PAL's high throughput should greatly facilitate structural genomic and proteomic studies. %Z 0969-2126 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. %U http://salilab.org/pdf/Dokudovskaya_Structure_2006.pdf %+ Laboratory of Cellular and Structural Biology, The Rockefeller University, 1230 York Avenue, New York, New York 10021, USA. %0 Journal Article %A Dong, G.Q. %A Calhoun, S. %A Fan, H. %A Kalyanaraman, C. %A London, N. %A Branch, M. %A Mashiyama, S. %A Jacobson, M. %A Shoichet, B. %A Babbitt, P. %A Armstrong, R. %A Sali, A. %D 2014 %T Prediction of substrates for glutathione transferases by covalent docking %B J Chem Inf Model %V 6 %P 1687-1699 %! Prediction of substrates for glutathione transferases by covalent docking %2 PMCID4068255 %M 24802635;PMCID:PMC4068255 %L 321 %F 321 %U http://salilab.org/pdf/Dong_JChemInfModel_2014.pdf %0 Journal Article %A Dong, G.Q. %A Fan, H. %A Schneidman-Duhovny, D. %A Webb, B. %A Sali, A. %D 2013 %T Optimized atomic statistical potentials: Assessment of protein interfaces and loops %B Bioinformatics %V 29 %P 3158-3166 %! Optimized atomic statistical potentials: Assessment of protein interfaces and loops %2 PMCID3842762 %M 24078704;PMCID:PMC3842762 %L 308 %F 308 %U http://salilab.org/pdf/Dong_Bioinformatics_2013.pdf %0 Journal Article %A Dvorak, J. %A Delcroix, M. %A Rossi, A. %A Vopalensky, V. %A Pospisek, M. %A Sedinova, M. %A Mikes, L. %A Sajid, M. %A Sali, A. %A Mckerrow, J.H. %A Horak, P. %A Caffrey, C.R. %D 2005 %T Multiple cathepsin B isoforms in schistosomula of Trichobilharzia regenti: Identification, characterization and putative role in migration and nutrition %B International Journal of Parasitology %V 35 %N 8 %P 895-910 %! Multiple cathepsin B isoforms in schistosomula of Trichobilharzia regenti: Identification, characterization and putative role in migration and nutrition %M 15950230 %L 150 %F 150 %U http://salilab.org/pdf/Dvorak_InternationalJournalofParasitology_2005.pdf %0 Journal Article %A Echeverria, I. %A Braberg, H. %A Krogan, N. J. %A Sali, A. %D 2023 %T Integrative structure determination of histones H3 and H4 using genetic interactions %B FEBS J %V 290 %N 10 %P 2565-2575 %! Integrative structure determination of histones H3 and H4 using genetic interactions %R 10.1111/febs.16435 %2 PMCID9481981 %M 35298864 %L 439 %F 439 %U https://salilab.org/pdf/Echeverria_FEBS_2022.pdf %0 Journal Article %A Eramian, D %A Eswar, N %A Shen, MY %A Sali, A %D 2008 %T How well can the accuracy of comparative protein structure models be predicted? %B Protein Sci %V 17 %N 11 %P 1881-1893 %8 Nov %! How well can the accuracy of comparative protein structure models be predicted? %@ 1469-896X %2 PMCID2578807 %M 18832340;PMCID:PMC2578807 %L 205 %F 205 %X Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the Calpha root-mean-squared deviation (RMSD) and native overlap (NO3.5A) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5A errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71. %U http://salilab.org/pdf/Eramian_ProteinSci_2008.pdf %+ Graduate Group in Biophysics, University of California at San Francisco, California 94158, USA. %G eng %0 Journal Article %A Eramian, D %A Shen, MY %A Devos, D %A Melo, F %A Sali, A %A Marti-Renom, MA %D 2006 %T A composite score for predicting errors in protein structure models %B Protein Sci %V 15 %N 7 %P 1653-1666 %8 Jul %! A composite score for predicting errors in protein structure models. %@ 0961-8368 %M 16751606 %L 163 %F 163 %K Models, Molecular Models, Theoretical Proteins %X Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling. %U http://salilab.org/pdf/Eramian_ProteinSci_2006.pdf %+ Graduate Group in Biophysics, Department of Biopharmaceutical Sciences, University of California at San Francisco 94158, USA. %G eng %0 Journal Article %A Erzberger, J. %A Stengel, F. %A Pellarin, R. %A Zhang, S. %A Schaefer, T. %A Aylett, C. %A Cimermancic, P. %A Boehringer, D. %A Sali, A. %A Aebersold, R. %A Ban, N. %D 2014 %T Molecular architecture of the 40S•eIF1•eIF3 translation initiation complex %B Cell %V 158 %P 1125-1135 %! Molecular architecture of the 40S•eIF1•eIF3 translation initiation complex %2 PMCID4151992 %M 25171412;PMCID:PMC4151992 %L 325 %F 325 %U http://salilab.org/pdf/Erzberger_Cell_2014.pdf %0 Journal Article %A Espadaler, J. %A Aragues, R. %A Eswar, N. %A Marti-Renom, M. A. %A Querol, E. %A Aviles, F. X. %A Sali, A. %A Oliva, B. %D 2005 %T Detecting remotely related proteins by their interactions and sequence similarity %B Proc Natl Acad Sci U S A %V 102 %N 20 %P 7151-7156 %8 May 17 %! Detecting remotely related proteins by their interactions and sequence similarity %M 15883372 %L 142 %F 142 %X The function of an uncharacterized protein is usually inferred either from its homology to, or its interactions with, characterized proteins. Here, we use both sequence similarity and protein interactions to identify relationships between remotely related protein sequences. We rely on the fact that homologous sequences share similar interactions, and, therefore, the set of interacting partners of the partners of a given protein is enriched by its homologs. The approach was benchmarked by assigning the fold and functional family to test sequences of known structure. Specifically, we relied on 1,434 proteins with known folds, as defined in the Structural Classification of Proteins (SCOP) database, and with known interacting partners, as defined in the Database of Interacting Proteins (DIP). For this subset, the specificity of fold assignment was increased from 54% for position-specific iterative blast to 75% for our approach, with a concomitant increase in sensitivity for a few percentage points. Similarly, the specificity of family assignment at the e-value threshold of 10(-8) was increased from 70% to 87%. The proposed method would be a useful tool for large-scale automated discovery of remote relationships between protein sequences, given its unique reliance on sequence similarity and protein-protein interactions. %Z 0027-8424 Journal Article %U http://salilab.org/pdf/Espadaler_ProcNatlAcadSciUSA_2005.pdf %+ Laboratori de Bioinformatica Estructural, Grup de Recerca en Informatica Biomedica-Institut Municipal d'Investigacio Medica (GRIB-IMIM), Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain. %0 Journal Article %A Espadaler, J %A Eswar, N %A Querol, E %A Aviles, FX %A Sali, A %A Marti-Renom, MA %A Oliva, B %D 2008 %T Prediction of enzyme function by combining sequence similarity and protein interactions %B BMC Bioinformatics %V 9 %P 249 %! Prediction of enzyme function by combining sequence similarity and protein interactions. %@ 1471-2105 %2 PMCID2430716 %M 18505562;PMCID:PMC2430716 %L 201 %F 201 %K Amino Acid Sequence Databases, Protein Enzymes Fuzzy Logic Pattern Recognition, Automated Predictive Value of Tests Protein Interaction Mapping Proteins Sequence Alignment Sequence Analysis, Protein Sequence Homology, Amino Acid Software Structure-Activity Relationship Substrate Specificity %X BACKGROUND: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. RESULTS: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. CONCLUSION: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone. %Z PMC2430716 %U http://salilab.org/pdf/Espadaler_BMCBioinformatics_2008.pdf %+ Laboratori de Bioinform‚Äö√Ѭ∞tica Estructural (GRIB), Departament de Ci‚àö√£ncies Experimentals i de la Salut, Universitat Pompeu Fabra-IMIM, 08003-Barcelona, Catalonia, Spain. wisl@bioinf.uab.es %G eng %0 Journal Article %A Eswar, N. %A Eramian, D. %A Webb, B. %A Shen, M. Y. %A Sali, A. %D 2008 %T Protein structure modeling with MODELLER %B Methods Mol Biol %V 426 %P 145-159 %! Protein structure modeling with MODELLER %O Methods in molecular biology (Clifton, N.J %@ 1064-3745 (Print) %M 18542861 %L 192 %F 192 %X Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences. %Z Journal Article United States %U http://salilab.org/pdf/Eswar_MethodsMolBiol_2008.pdf %+ Department of Biopharmaceutical Sciences and California Institute for Quantitative Biomedical Research, University of California at San Francisco, San Francisco, CA, USA. %G eng %0 Journal Article %A Eswar, N. %A John, B. %A Mirkovic, N. %A Fiser, A. %A Ilyin, V. A. %A Pieper, U. %A Stuart, A. C. %A Marti-Renom, M. A. %A Madhusudhan, M. S. %A Yerkovich, B. %A Sali, A. %D 2003 %T Tools for comparative protein structure modeling and analysis %B Nucleic Acids Res %V 31 %N 13 %P 3375-3380 %! Tools for comparative protein structure modeling and analysis %@ 0305-1048 %R 10.1093/nar/gkg543 %M 12824331 %L 123 %F 123 %X The following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution. %U http://salilab.org/pdf/Eswar_NucleicAcidsRes_2003.pdf %0 Book Section %A Eswar, N. %A Sali, A. %D 2009 %T Protein Structure Modeling %E Sussman, J. L. %E Spadon, P. %B From Molecules to Medicine, Structure of Biological Macromolecules and Its Relevance in Combating New Diseases and Bioterrorism %C Dordrecht, The Netherlands %I Springer-Verlag %P 139-151 %S NATO Science for Peace and Security Series - A: Chemistry and Biology %! Protein Structure Modeling %@ 978-90-481-2338-4 %L 212 %F 212 %Z 9 %U http://salilab.org/pdf/Eswar_NATOScience_2009.pdf %0 Journal Article %A Eswar, N. %A Webb, B. %A Marti-Renom, M. A. %A Madhusudhan, M. S. %A Eramian, D. %A Shen, M. Y. %A Pieper, U. %A Sali, A. %D 2007 %T Comparative protein structure modeling using MODELLER %B Curr Protoc Protein Sci %V Chapter 2 %P Unit 2.9 %8 Nov %! Comparative protein structure modeling using MODELLER %O Current protocols in protein science / editorial board, John E. Coligan ... [et al %@ 1934-3663 (Electronic) %M 18429317 %L 172 %F 172 %X Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. %Z P01 A135707/United States PHS P01 GM71790/GM/United States NIGMS R01 GM54762/GM/United States NIGMS U54 GM62529/GM/United States NIGMS Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States %U http://salilab.org/pdf/Eswar_CurrentProtocolsinProteinScience_2007.pdf %+ University of California at San Francisco, San Francisco, California, USA. %G eng %0 Book Section %A Eswar, N %A Sali, A. %D 2007 %T Comparative Modeling of Drug Target Proteins %E Taylor, J. %E Triggle, D. %E Mason, J. S. %B Volume 4 Computer-Assisted Drug Design, Comprehensive Medicinal Chemistry II %C Oxford, UK %I Elsevier Ltd %P 215-236 %! Comparative Modeling of Drug Target Proteins %L 175 %F 175 %U http://salilab.org/pdf/Eswar_ComprMedChem_2007.pdf %0 Journal Article %A Eswar, N %A Webb, B %A Marti-Renom, MA %A Madhusudhan, MS %A Eramian, D %A Shen, MY %A Pieper, U %A Sali, A %D 2006 %T Comparative protein structure modeling using Modeller %B Curr Protoc Bioinformatics %V Chapter 5 %P Unit 5.6 %8 Oct %! Comparative protein structure modeling using Modeller. %@ 1934-340X %M 18428767 %L 165 %F 165 %K Algorithms Amino Acid Sequence Computer Simulation Crystallography Models, Chemical Models, Molecular Molecular Sequence Data Protein Conformation Protein Folding Proteins Sequence Analysis, Protein Software %X Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. %U http://salilab.org/pdf/Eswar_CurrentProtocolsinBioinformatics_2006.pdf %+ University of California at San Francisco, San Francisco, California, USA. %G eng %0 Journal Article %A Eyrich, V. A. %A Marti-Renom, M. A. %A Przybylski, D. %A Madhusudhan, M. S. %A Fiser, A. %A Pazos, F. %A Valencia, A. %A Sali, A. %A Rost, B. %D 2001 %T EVA: continuous automatic evaluation of protein structure prediction servers %B Bioinformatics %V 17 %N 12 %P 1242-1243 %8 Dec %! EVA: continuous automatic evaluation of protein structure prediction servers %M 11751240 %L 92 %F 92 %K Automation Internet *Protein Conformation Proteins/*analysis *Software %X Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY: http://cubic.bioc.columbia.edu/eva. CONTACT: eva@cubic.bioc.columbia.edu %Z 1367-4803 Journal Article %U http://salilab.org/pdf/Eyrich_Bioinformatics_2001.pdf %+ Department of Chemistry, Columbia University, 3000 Broadway MC 3136, New York, NY 10027, USA. eva@cubic.bioc.columbia.edu %0 Journal Article %A Fan, H. %A Hitchcock, D. %A Seidel, R. %A Hillerich, B. %A Lin, H. %A Almo, S. %A Sali, A. %A Shoichet, B. %A Raushel, F. %D 2013 %T The Assignment of Pterin Deaminase Activity to an Enzyme of Unknown Function Guided by Homology Modeling and Docking %B J Am Chem Soc %V 135 %P 795-803 %! The Assignment of Pterin Deaminase Activity to an Enzyme of Unknown Function Guided by Homology Modeling and Docking %2 PMCID3557803 %M 23256477;PMCID:PMC3557803 %L 289 %F 289 %U http://salilab.org/pdf/Fan_JAmChemSoc_2013.pdf %0 Journal Article %A Fan, H. %A Irwin, J.J. %A Webb, B.M. %A Klebe, G. %A Shoichet, B. %A Sali, A. %D 2009 %T Molecular Docking Screens Using Comparative Models of Proteins %B J Chem Inf Model %V 49 %P 2512-2527 %! Molecular Docking Screens Using Comparative Models of Proteins %2 PMCID2790034 %M 19845314;PMCID:PMC2790034 %L 232 %F 232 %U http://salilab.org/pdf/Fan_JChemInfModel_2009.pdf %0 Journal Article %A Fan, H. %A Irwin, J. %A Sali, A. %D 2012 %T Virtual Ligand Screening Against Comparative Protein Structure Models %B Methods in Molecular Biology %V 819 %P 105-126 %! Virtual Ligand Screening Against Comparative Protein Structure Models %2 PMCID3386294 %M 22183533;PMCID:PMC3386294 %L 268 %F 268 %U http://salilab.org/pdf/Fan_MethodsMolBiol_2012.pdf %0 Journal Article %A Fan, H. %A Schneidman, D. %A Irwin, J.J. %A Dong, G. %A Shoichet, B. %A Sali, A. %D 2011 %T Statistical Potential for Modeling and Ranking Protein-Ligand Interactions %B J Chem Inf Model %V 51 %P 3078-3092 %! Statistical Potential for Modeling and Ranking Protein-Ligand Interactions %2 PMCID3246566 %M 22014038;PMCID:PMC3246566 %L 257 %F 257 %U http://salilab.org/pdf/Fan_JChemInfModel_2011.pdf %W https://github.com/salilab/ligscore %0 Journal Article %A Fernandez-Martinez, J. %A Kim, S.J. %A Shi, Y. %A Upla, P. %A Pellarin, R. %A Gagnon, M. %A Chemmama, I.E. %A Wang, J. %A Nudelman, I. %A Zhang, W. %A Williams, R. %A Rice, W.J. %A Stokes, D.L. %A Zenklusen, D. %A Chait, B.T. %A Sali, A. %A Rout, M.P. %D 2016 %T Structure and Function of the Nuclear Pore Complex Cytoplasmic mRNA Export Platform %B Cell %V 167 %N 5 %P 1215-1228 %! Structure and Function of the Nuclear Pore Complex Cytoplasmic mRNA Export Platform %R 10.1016/j.cell.2016.10.028 %2 PMCID5130164 %M 27839866 %L 360 %F 360 %U https://salilab.org/pdf/Fernandez-Martinez_Cell_2016.pdf %0 Journal Article %A Fernandez-Martinez, J. %A Phillips, J. %A Sekedat, M. %A Diaz-Avalos, R. %A Velazquez-Muriel, J. %A Franke, J. %A Williams, R. %A Stokes, D. %A Chait, B. %A Sali, A. %A Rout, M. %D 2012 %T Structure-function Map for a Heptameric Component of the Nuclear Pore Complex %B J Cell Biol %V 196 %P 419-434 %! Structure-function Map for a Heptameric Component of the Nuclear Pore Complex %2 PMCID3283990 %M 22331846;PMCID:PMC3283990 %L 269 %F 269 %U http://salilab.org/pdf/Fernandez-Martinez_JCellBiol_2012.pdf %0 Journal Article %A Feyfant, E %A Sali, A %A Fiser, A %D 2007 %T Modeling mutations in protein structures %B Protein Sci %V 16 %N 9 %P 2030-2041 %8 Sep %! Modeling mutations in protein structures. %@ 0961-8368 %M 17766392 %L 178 %F 178 %K Automation Computer Simulation Crystallography, X-Ray Genetic Variation Models, Molecular Point Mutation Protein Conformation Protein Structure, Secondary Proteins %X We describe an automated method for the modeling of point mutations in protein structures. The protein is represented by all non-hydrogen atoms. The scoring function consists of several types of physical potential energy terms and homology-derived restraints. The optimization method implements a combination of conjugate gradient minimization and molecular dynamics with simulated annealing. The testing set consists of 717 pairs of known protein structures differing by a single mutation. Twelve variations of the scoring function were tested in three different environments of the mutated residue. The best-performing protocol optimizes all the atoms of the mutated residue, with respect to a scoring function that includes molecular mechanics energy terms for bond distances, angles, dihedral angles, peptide bond planarity, and non-bonded atomic contacts represented by Lennard-Jones potential, dihedral angle restraints derived from the aligned homologous structure, and a statistical potential for non-bonded atomic interactions extracted from a large set of known protein structures. The current method compares favorably with other tested approaches, especially when predicting long and flexible side-chains. In addition to the thoroughness of the conformational search, sampled degrees of freedom, and the scoring function type, the accuracy of the method was also evaluated as a function of the flexibility of the mutated side-chain, the relative volume change of the mutated residue, and its residue type. The results suggest that further improvement is likely to be achieved by concentrating on the improvement of the scoring function, in addition to or instead of increasing the variety of sampled conformations. %U http://salilab.org/pdf/Feyfant_ProteinSci_2007.pdf %+ Wyeth Research, Chemical and Screening Sciences, Cambridge, Massachusetts 02421, USA. %G eng %0 Journal Article %A Field, M. %A Sali, A. %A Rout, M. %D 2011 %T On a bender: Bars, ESCRTs, COPs, and finally getting your coat %B J Cell Biol %V 193 %P 963-972 %! On a bender: Bars, ESCRTs, COPs, and finally getting your coat %2 PMCID3115789 %M 21670211;PMCID:PMC3115789 %L 256 %F 256 %U http://salilab.org/pdf/Field_JCellBiol_2011.pdf %0 Journal Article %A Fiser, A. %A Do, R. K. G. %A Sali, A. %D 2000 %T Modeling of loops in protein structures %B Protein Sci %V 9 %N 9 %P 1753-1773 %! Modeling of loops in protein structures %@ 0961-8368 %M 11045621 %L 85 %F 85 %X Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts chat depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a Function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had <2 Angstrom RMSD error for the mainchain N, C-alpha, C, and O atoms; the average accuracies were 0.59 +/- 0.05, 1.16 +/- 0.10, and 2.61 +/- 0.16 Angstrom, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 Angstrom, the average loop prediction error increased by 18, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by (1) comparing it with one of the most successful previously described methods, and (2) describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling. %U http://salilab.org/pdf/Fiser_ProteinSci_2000.pdf %0 Journal Article %A Fiser, A. %A Feig, M. %A Brooks, C. L. %A Sali, A. %D 2002 %T Evolution and physics in comparative protein structure modeling %B Accounts of Chemical Research %V 35 %N 6 %P 413-421 %! Evolution and physics in comparative protein structure modeling %@ 0001-4842 %R 10.1021/ar010061h %M 12069626 %L 109 %F 109 %X From a physical perspective, the native structure of a protein is a consequence of physical forces acting on the protein and solvent atoms during the folding process. From a biological perspective, the native structure of proteins is a result of evolution over millions of years. Correspondingly, there are two types of protein structure prediction methods, de novo prediction and comparative modeling. We review comparative protein structure modeling and discuss the incorporation of physical considerations into the modeling process. A good starting point for achieving this aim is provided by comparative modeling by satisfaction of spatial restraints. Incorporation of physical considerations is illustrated by an inclusion of solvation effects into the modeling of loops. %U http://salilab.org/pdf/Fiser_AccountsofChemicalResearch_2002.pdf %0 Book Section %A Fiser, A. %A Sali, A. %D 2003 %T Comparative Protein Structure Modeling %E Chasman, D. %B Protein Structure %I Marcel Dekker, Inc. %P 167-206 %! Comparative Protein Structure Modeling %L 119 %F 119 %Z TY - CHAP %U http://salilab.org/pdf/Fiser__2003.pdf %+ New York %0 Journal Article %A Fiser, A. %A Sali, A. %D 2003 %T Modeller: generation and refinement of homology-based protein structure models %B Methods Enzymol %V 374 %P 461-491 %! Modeller: generation and refinement of homology-based protein structure models %M 14696385 %L 116 %F 116 %K Amino Acid Sequence Animals *Bacterial Proteins Carrier Proteins/chemistry/genetics Databases, Protein L-Lactate Dehydrogenase/chemistry/genetics *Models, Molecular Molecular Sequence Data Protein Structure, Tertiary Proteins/*chemistry/genetics Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Alignment *Sequence Homology *Software Trichomonas vaginalis/enzymology %Z 0076-6879 Journal Article %U http://salilab.org/pdf/Fiser_MethodsEnzymol_2003.pdf %+ Department of Biochemistry and Seaver Foundation Center for Bioinformatics, Albert Einstein College of Medicine, Bronz, New York 10461, USA. %0 Journal Article %A Fiser, A. %A Sali, A. %D 2003 %T ModLoop: automated modeling of loops in protein structures %B Bioinformatics %V 19 %N 18 %P 2500-2501 %! ModLoop: automated modeling of loops in protein structures %@ 1367-4803 %R 10.1093/bioinformatics/btg362 %M 14668246 %L 128 %F 128 %X ModLoop is a web server for automated modeling of loops in protein structures. The input is the atomic coordinates of the protein structure in the Protein Data Bank format, and the specification of the starting and ending residues of one or more segments to be modeled, containing no more than 20 residues in total. The output is the coordinates of the non-hydrogen atoms in the modeled segments. A user provides the input to the server via a simple web interface, and receives the output by e-mail. The server relies on the loop modeling routine in MODELLER that predicts the loop conformations by satisfaction of spatial restraints, without relying on a database of known protein structures. For a rapid response, ModLoop runs on a cluster of Linux PC computers. %U http://salilab.org/pdf/Fiser_Bioinformatics_2003.pdf %W https://github.com/salilab/modloop %0 Book Section %A Fiser, A. %A Sanchez, R. %A Melo, F. %A Sali, A. %D 2000 %T Comparative protein structure modeling %E Watanabe, M. %E Roux, B. %E Mackerell, A. %E Becker, O. %B Computational Biochemistry and Biophysics %I Marcel Dekker %P 275-312 %! Comparative protein structure modeling %L 89 %F 89 %Z TY - CHAP %U http://salilab.org/pdf/Fiser_CompBioChemPhys_2000.pdf %0 Journal Article %A Fornes, O %A Aragues, R %A Espadaler, J %A Marti-Renom, MA %A Sali, A %A Oliva, B %D 2009 %T ModLink+: Improving fold recognition by using protein-protein interactions %B Bioinformatics %V 25 %P 1506-1512 %8 Apr %! ModLink+: Improving fold recognition by using protein-protein interactions. %@ 1460-2059 %2 PMCID2687990 %M 19357100;PMCID:PMC2687990 %L 224 %F 224 %X MOTIVATION: Several strategies have been developed to predict the fold of a target protein sequence, most of which are based on aligning the target sequence to other sequences of known structure. Previously, we demonstrated that the consideration of protein-protein interactions significantly increases the accuracy of fold assignment compared to PSI-BLAST sequence comparisons. A drawback of our method was the low number of proteins to which a fold could be assigned. Here, we present an improved version of the method that addresses this limitation. We also compare our method to other state-of-the-art fold assignment methodologies. RESULTS: Our approach (ModLink+) has been tested on 3,716 proteins with domain folds classified in the Structural Classification Of Proteins (SCOP) as well as known interacting partners in the Database of Interacting Proteins (DIP). For this test set, the ratio of success (PPV) on fold assignment increases from 75% for PSI-BLAST, 83% for HHSearch and 81% for PRC to more than 90% for ModLink+ at the e-value cutoff of 10(-3). Under this e-value, ModLink+ can assign a fold to 30-45% of the proteins in the test set, while our previous method could cover less than 25%. When applied to 6,384 proteins with unknown fold in the yeast proteome, ModLink+ combined with PSI-BLAST assigns a fold for domains in 3,738 proteins, while PSI-BLAST alone only covers 2,122 proteins, HHSearch 2,969 and PRC 2,826 proteins, using a threshold e-value that would represent a PPV higher than 82% for each method in the test set. AVAILABILITY: The ModLink+ server is freely accessible in the World Wide Web at http://sbi.imim.es/modlink/. CONTACT: boliva@imim.es. %U http://salilab.org/pdf/Fornes_Bioinformatics_2009.pdf %+ Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, Parc de Recerca Biom‚àö√£dica de Barcelona (PRBB), Barcelona, Catalonia, Spain. %G Eng %0 Journal Article %A Forster, F. %A Webb, B. %A Krukenberg, K. A. %A Tsuruta, H. %A Agard, D. A. %A Sali, A. %D 2008 %T Integration of small-angle X-ray scattering data into structural modeling of proteins and their assemblies %B J Mol Biol %V 382 %N 4 %P 1089-1106 %! Integration of small-angle X-ray scattering data into structural modeling of proteins and their assemblies %@ 0022-2836 %R 10.1016/j.jmb.2008.07.074 %2 PMCID2745287 %M 18694757;PMCID:PMC2745287 %L 206 %F 206 %X A major challenge in structural biology is to determine the configuration of domains and proteins in multidomain proteins and assemblies, respectively. All available data should be considered to maximize the accuracy and precision of these models. Small-angle X-ray scattering (SAXS) efficiently provides low-resolution experimental data about the shapes of proteins and their assemblies. Thus, we integrated SANS profiles into our software for modeling proteins and their assemblies by satisfaction of spatial restraints. Specifically, we modeled the quaternary structures of multidomain proteins with structurally defined rigid domains as well as quaternary structures of binary complexes of structurally defined rigid proteins. In addition to SAXS profiles and the component structures, we used stereochemical restraints and an atomic distance-dependent statistical potential. The scoring function is optimized by a biased Monte Carlo protocol, including quasi-Newton and simulated annealing schemes. The final prediction corresponds to the best scoring solution in the largest cluster of many independently calculated solutions. To quantify how well the quaternary structures are determined based on their SANS profiles, we used a benchmark of 12 simulated examples as well as an experimental SANS profile of the homotetramer D-xylose isomerase. Optimization of the SAXS-dependent scoring function generally results in accurate models, if sufficiently precise approximations for the constituent rigid bodies are available; otherwise, the best scoring models can have significant errors. Thus, SAXS profiles can play a useful role in the structural characterization of proteins and assemblies if they are combined with additional data and used judiciously. Our integration of a SANS profile into modeling by satisfaction of spatial restraints will facilitate further integration of different kinds of data or structure determination of proteins and their assemblies. (C) 2008 Elsevier Ltd. All rights reserved. %U http://salilab.org/pdf/Forster_JMolBiol_2008.pdf %0 Journal Article %A Forster, F %A Lasker, K %A Beck, F %A Nickell, S %A Sali, A %A Baumeister, W %D 2009 %T An Atomic Model AAA-ATPase/20S core particle sub-complex of the 26S proteasome %B Biochem Biophys Res Commun %V 388 %P 228-233 %! An Atomic Model AAA-ATPase/20S core particle sub-complex of the 26S proteasome %2 PMCID2771176 %M 19653995;PMCID:PMC Journal- In Process %L 233 %F 233 %U http://salilab.org/pdf/Forster_BiochemBiophysResCommun_2009.pdf %0 Journal Article %A Forster, F %A Lasker, K %A Nickell, S %A Sali, A %A Baumeister, W %D 2010 %T Toward an integrated structural model of the 26S proteasome %B Mol Cell Proteomics %V 9 %P 1666-1677 %! Toward an integrated structural model of the 26S proteasome %2 PMCID2938054 %M 20467039;PMCID:PMC2938054 %L 247 %F 247 %U http://salilab.org/pdf/Forster_MolCellProteomics_2010.pdf %0 Journal Article %A Fromme, P. %A Sali, A. %D 2016 %T Editorial overview: Biophysical and molecular biological methods %B Curr Opin Struct Biol %V 40 %P ix-xi %! Editorial overview: Biophysical and molecular biological methods %R 10.1016/j.sbi.2016.11.015 %M 27908508 %L 348 %F 348 %U https://salilab.org/pdf/Fromme_CurrOpinStructBiol_2016.pdf %0 Journal Article %A Gaber, A %A Kim, SJ %A Kaake, RM %A Bencina, M %A Krogan, N %A Sali, A %A Pavsic, M %A Lenarcic, B %D 2018 %T EpCAM homo-oligomerization is not the basis for its role in cell-cell adhesion %B Sci Rep %V 8 %N 1 %P 13269 %! EpCAM homo-oligomerization is not the basis for its role in cell-cell adhesion %R 10.1038/s41598-018-31482-7 %2 PMCID6125409 %M 30185875 %L 390 %F 390 %U https://salilab.org/pdf/Gaber_SciRep_2018.pdf %0 Journal Article %A Ganesan, S. J. %A Feyder, M. J. %A Chemmama, I. E. %A Fang, F. %A Rout, M. P. %A Chait, B. T. %A Shi, Y. %A Munson, M. %A Sali, A. %D 2020 %T Integrative Structure and Function of the Yeast Exocyst Complex %B Protein Sci %V 29 %N 6 %P 1486-1501 %7 2020/04/02 %8 Apr %! Integrative Structure and Function of the Yeast Exocyst Complex %@ 1469-896X %R 10.1002/pro.3863 %2 PMCID7255525 %M 32239688 %L 401 %F 401 %K EM SNAREs Yeast exocyst complex chemical cross-linking mass spectrometry (CXMS) exocytosis integrative modeling membrane fusion protein cross-linking structural models %X Exocyst is an evolutionarily conserved hetero-octameric tethering complex that plays a variety of roles in membrane trafficking, including exocytosis, endocytosis, autophagy, cell polarization, cytokinesis, pathogen invasion, and metastasis. Exocyst serves as a platform for interactions between the Rab, Rho, and Ral small GTPases, SNARE proteins, and Sec1/Munc18 regulators that coordinate spatial and temporal fidelity of membrane fusion. However, its mechanism is poorly described at the molecular level. Here, we determine the molecular architecture of the yeast exocyst complex by an integrative approach, based on a 3D density map from negative-stain electron microscopy (EM) at ~16 å resolution, 434 DSS and EDC cross-links from chemical-crosslinking mass spectrometry, and partial atomic models of the 8 subunits. The integrative structure is validated by a previously determined cryo-EM structure, cross-links, and distances from in vivo fluorescence microscopy. Our subunit configuration is consistent with the cryo-EM structure, except for Sec5. While not observed in the cryo-EM map, the integrative model localizes the N-terminal half of Sec3 near the Sec6 subunit. Limited proteolysis experiments suggest that the conformation of Exo70 is dynamic, which may have functional implications for SNARE and membrane interactions. This study illustrates how integrative modeling based on varied low-resolution structural data can inform biologically relevant hypotheses, even in the absence of high-resolution data. This article is protected by copyright. All rights reserved. %U https://salilab.org/pdf/Ganesan_ProtSci_2020.pdf %G eng %0 Journal Article %A Gao, H. X. %A Sengupta, J. %A Valle, M. %A Korostelev, A. %A Eswar, N. %A Stagg, S. M. %A Van Roey, P. %A Agrawal, R. K. %A Harvey, S. C. %A Sali, A. %A Chapman, M. S. %A Frank, J. %D 2003 %T Study of the structural dynamics of the E-coli 70S ribosome using real-space refinement %B Cell %V 113 %N 6 %P 789-801 %! Study of the structural dynamics of the E-coli 70S ribosome using real-space refinement %@ 0092-8674 %M 12809609 %L 124 %F 124 %X Cryo-EM density maps showing the 70S ribosome of E. coli in two different functional states related by a ratchet-like motion were analyzed using real-space refinement. Comparison of the two resulting atomic models shows that the ribosome changes from a compact structure to a looser one, coupled with the rearrangement of many of the proteins. Furthermore, in contrast to the unchanged inter-subunit bridges formed wholly by RNA, the bridges involving proteins undergo large conformational changes following the ratchet-like motion, suggesting an important role of ribosomal proteins in facilitating the dynamics of translation. %U http://salilab.org/pdf/Gao_Cell_2003.pdf %0 Journal Article %A Geier, E. %A Schlessinger, A. %A Fan, H. %A Gable, J. %A Irwin, J. %A Sali, A. %A Giacomini, K. %D 2013 %T Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1 %B Proc Natl Acad Sci USA %V 110 %P 5480-5485 %! Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1 %2 PMCID3619328 %M 23509259;PMCID:PMC3619328 %L 301 %F 301 %U http://salilab.org/pdf/Geier_ProcNatlAcadSciUSA_2013.pdf %0 Journal Article %A Gerlt, J. %A Allen, K. %A Almo, S. %A Armstrong, R. %A Babbitt, P. %A Cronan, J. %A Dunaway-Mariano, D. %A Imker, H. %A Jacobson, M. %A Minor, W. %A Poulter, C. %A Raushel, F. %A Sali, A. %A Shoichet, B. %A Sweedler, J. %D 2011 %T The Enzyme Function Initiative %B Biochemistry %V 50 %P 9950-9962 %! The Enzyme Function Initiative %2 PMCID3238057 %M 21999478;PMCID:PMC3238057 %L 265 %F 265 %U http://salilab.org/pdf/Gerlt_Biochemistry_2011.pdf %0 Journal Article %A Ghildyal, N. %A Friend, D. S. %A Stevens, R. L. %A Austen, K. F. %A Huang, C. %A Penrose, J. F. %A Sali, A. %A Gurish, M. F. %D 1996 %T Fate of two mast cell tryptases in V3 mastocytosis and normal BALB/c mice undergoing passive systemic anaphylaxis: prolonged retention of exocytosed mMCP-6 in connective tissues, and rapid accumulation of enzymatically active mMCP-7 in the blood %B The Journal of Experimental Medicine %V 184 %N 3 %P 1061-1073 %8 Sep 1 %! Fate of two mast cell tryptases in V3 mastocytosis and normal BALB/c mice undergoing passive systemic anaphylaxis: prolonged retention of exocytosed mMCP-6 in connective tissues, and rapid accumulation of enzymatically active mMCP-7 in the blood %M 9064323 %L 48 %F 48 %K Anaphylaxis/*enzymology Animals Electrophoresis, Polyacrylamide Gel *Exocytosis Hydrogen-Ion Concentration Inflammation Mediators/*metabolism Mast Cells/*enzymology Mastocytosis/*enzymology Mice Mice, Inbred BALB C Models, Molecular Mutagenesis, Site-Directed Peptide Mapping Receptors, IgG/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Serine Endopeptidases/*metabolism %X The mouse mast cell protease granule tryptases designated mMCP-6 and mMCP-7 are encoded by highly homologous genes that reside on chromosome 17. Because these proteases are released when mast cells are activated, we sought a basis for distinctive functions by examining their fates in mice undergoing passive systemic anaphylaxis. 10 min-1 h after antigen (Ag) was administered to immunoglobulin (Ig)E-sensitized mice, numerous protease/proteoglycan macromolecular complexes appeared in the extracellular matrix adjacent to most tongue and heart mast cells of normal BALB/c mice and most spleen and liver mast cells of V3 mastocytosis mice. These complexes could be intensively stained by anti-mMCP-6 Ig but not by anti-mMCP-7 Ig. Shortly after Ag challenge of V3 mastocytosis mice, large amounts of properly folded, enzymatically active mMCP-7 were detected in the plasma. This plasma-localized tryptase was approximately 150 kD in its multimeric state and approximately 32 kD in its monomeric state, possessed an NH2 terminus identical to that of mature mMCP-7, and was not covalently bound to any protease inhibitor. Comparative protein modeling and electrostatic calculations disclosed that mMCP-6 contains a prominent Lys/Arg-rich domain on its surface, distant from the active site. The absence of this domain in mMCP-7 provides an explanation for its selective dissociation from the exocytosed macromolecular complex. The retention of exocytosed mMCP-6 in the extracellular matrix around activated tissue mast cells suggests a local action. In contrast, the rapid dissipation of mMCP-7 from granule cores and its inability to be inactivated by circulating protease inhibitors suggests that this tryptase cleaves proteins located at more distal sites. %Z 0022-1007 Journal Article %U http://salilab.org/pdf/Ghildyal_TheJournalofExperimentalMedicine_1996.pdf %+ Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. %0 Journal Article %A Giacomini, K. %A Yee, S.W. %A Peral, L.F. %A Alentorn-Moron, P. %A Fontsere, C. %A Ceylan, M. %A Koleske, M. %A Handin, N. %A Artegoitia, V. %A Lara, G. %A Chien, H. %A Zhou, X. %A Dainat, J. %A Zalevsky, A. %A Sali, A. %A Brand, C. %A Capra, J. %A Artursson, P. %A Newman, J. %A Marques-Bonet, T. %A Wolfreys, F. %A Yang, J. %A Gestwicki, J. %D 2024 %T Illuminating the Function of the Orphan Transporter, SLC22A10, in Humans and Other Primates %B Nat Comm, in press %! Illuminating the Function of the Orphan Transporter, SLC22A10, in Humans and Other Primates %2 PMCID10543398 %M 37790518 %L 456 %F 456 %0 Journal Article %A Goble, A.M. %A Fan, H. %A Sali, A. %A Raushel, F.M. %D 2011 %T Discovery of a Cytokinin Deaminase %B ACS Chem Biol %V 6 %P 1036-1040 %! Discovery of a Cytokinin Deaminase %2 PMCID3199332 %M 21823622;PMCID:PMC3199332 %L 264 %F 264 %U http://salilab.org/pdf/Goble_ACSChemBiol_2011.pdf %0 Journal Article %A Goble, A. %A Toro, R. %A Li, X. %A Ornelas, A. %A Fan, H. %A Eswaramoorthy, S. %A Patskovsky, Y. %A Hillerich, B. %A Seidel, R. %A Sali, A. %A Shoichet, B. %A Almo, S. %A Swaminathan, S. %A Tanner, M. %A Raushel, F. %D 2013 %T Deamination of 6-Aminodeoxyfutalosine in Menaquinone Biosynthesis by Distantly Related Enzymes %B Biochemistry %V 52 %P 6525-6536 %! Deamination of 6-Aminodeoxyfutalosine in Menaquinone Biosynthesis by Distantly Related Enzymes %2 PMCID3813303 %M 23972005;PMCID:PMC3813303 %L 311 %F 311 %U http://salilab.org/pdf/Goble_Biochemistry_2013.pdf %0 Journal Article %A Gopal, S. %A Schroeder, M. %A Pieper, U. %A Sczyrba, A. %A Aytekin-Kurban, G. %A Bekiranov, S. %A Fajardo, J. E. %A Eswar, N. %A Sanchez, R. %A Sali, A. %A Gaasterland, T. %D 2001 %T Homology-based annotation yields 1,042 new candidate genes in the Drosophila melanogaster genome %B Nat Genet %V 27 %N 3 %P 337-340 %! Homology-based annotation yields 1,042 new candidate genes in the Drosophila melanogaster genome %@ 1061-4036 %M 11242120 %L 91 %F 91 %X The approach to annotating a genome critically affects the number acid accuracy of genes identified in the genome sequence. Genome annotation based on stringent gene identification is prone to underestimate the complement of genes encoded in a genome. In contrast, over-prediction of putative genes followed by exhaustive computational sequence, motif and structural homology search will find rarely expressed, possibly unique, new genes at the risk of including non-functional genes. We developed a two-stage approach that combines the merits of stringent genome annotation with the benefits of over-prediction. First we identify plausible genes regardless of matches with EST, cDNA or protein sequences from the organism (stage 1). In the second stage, proteins predicted from the plausible genes are compared at the protein level with EST. cDNA and protein sequences, and protein structures from other organisms (stage 2). Remote but biologically meaningful protein sequence or structure homologies provide supporting evidence for genuine genes. The method, applied to the Drosophila melanogaster genome, validated 1,042 novel candidate genes after filtering 19,410 plausible genes, of which 12,124 matched the original 13,601 annotated genes(1). This annotation strategy is applicable to genomes of all organisms, including human. %U http://salilab.org/pdf/Gopal_NatGenet_2001.pdf %0 Journal Article %A Gordon, D. E. %A Hiatt, J. %A Bouhaddou, M. %A Rezelj, V. V. %A Ulferts, S. %A Braberg, H. %A Jureka, A. S. %A Obernier, K. %A Guo, J. Z. %A Batra, J. %A Kaake, R. M. %A Weckstein, A. R. %A Owens, T. W. %A Gupta, M. %A Pourmal, S. %A Titus, E. W. %A Cakir, M. %A Soucheray, M. %A McGregor, M. %A Cakir, Z. %A Jang, G. %A O'Meara, M. J. %A Tummino, T. A. %A Zhang, Z. %A Foussard, H. %A Rojc, A. %A Zhou, Y. %A Kuchenov, D. %A Hüttenhain, R. %A Xu, J. %A Eckhardt, M. %A Swaney, D. L. %A Fabius, J. M. %A Ummadi, M. %A Tutuncuoglu, B. %A Rathore, U. %A Modak, M. %A Haas, P. %A Haas, K. M. %A Naing, Z. Z. C. %A Pulido, E. H. %A Shi, Y. %A Barrio-Hernandez, I. %A Memon, D. %A Petsalaki, E. %A Dunham, A. %A Marrero, M. C. %A Burke, D. %A Koh, C. %A Vallet, T. %A Silvas, J. A. %A Azumaya, C. M. %A Billesbølle, C. %A Brilot, A. F. %A Campbell, M. G. %A Diallo, A. %A Dickinson, M. S. %A Diwanji, D. %A Herrera, N. %A Hoppe, N. %A Kratochvil, H. T. %A Liu, Y. %A Merz, G. E. %A Moritz, M. %A Nguyen, H. C. %A Nowotny, C. %A Puchades, C. %A Rizo, A. N. %A Schulze-Gahmen, U. %A Smith, A. M. %A Sun, M. %A Young, I. D. %A Zhao, J. %A Asarnow, D. %A Biel, J. %A Bowen, A. %A Braxton, J. R. %A Chen, J. %A Chio, C. M. %A Chio, U. S. %A Deshpande, I. %A Doan, L. %A Faust, B. %A Flores, S. %A Jin, M. %A Kim, K. %A Lam, V. L. %A Li, F. %A Li, J. %A Li, Y. L. %A Li, Y. %A Liu, X. %A Lo, M. %A Lopez, K. E. %A Melo, A. A. %A Moss, F. R. %A Nguyen, P. %A Paulino, J. %A Pawar, K. I. %A Peters, J. K. %A Pospiech, T. H. %A Safari, M. %A Sangwan, S. %A Schaefer, K. %A Thomas, P. V. %A Thwin, A. C. %A Trenker, R. %A Tse, E. %A Tsui, T. K. M. %A Wang, F. %A Whitis, N. %A Yu, Z. %A Zhang, K. %A Zhang, Y. %A Zhou, F. %A Saltzberg, D. %A Hodder, A. J. %A Shun-Shion, A. S. %A Williams, D. M. %A White, K. M. %A Rosales, R. %A Kehrer, T. %A Miorin, L. %A Moreno, E. %A Patel, A. H. %A Rihn, S. %A Khalid, M. M. %A Vallejo-Gracia, A. %A Fozouni, P. %A Simoneau, C. R. %A Roth, T. L. %A Wu, D. %A Karim, M. A. %A Ghoussaini, M. %A Dunham, I. %A Berardi, F. %A Weigang, S. %A Chazal, M. %A Park, J. %A Logue, J. %A McGrath, M. %A Weston, S. %A Haupt, R. %A Hastie, C. J. %A Elliott, M. %A Brown, F. %A Burness, K. A. %A Reid, E. %A Dorward, M. %A Johnson, C. %A Wilkinson, S. G. %A Geyer, A. %A Giesel, D. M. %A Baillie, C. %A Raggett, S. %A Leech, H. %A Toth, R. %A Goodman, N. %A Keough, K. C. %A Lind, A. L. %A Klesh, R. J. %A Hemphill, K. R. %A Carlson-Stevermer, J. %A Oki, J. %A Holden, K. %A Maures, T. %A Pollard, K. S. %A Sali, A. %A Agard, D. A. %A Cheng, Y. %A Fraser, J. S. %A Frost, A. %A Jura, N. %A Kortemme, T. %A Manglik, A. %A Southworth, D. R. %A Stroud, R. M. %A Alessi, D. R. %A Davies, P. %A Frieman, M. B. %A Ideker, T. %A Abate, C. %A Jouvenet, N. %A Kochs, G. %A Shoichet, B. %A Ott, M. %A Palmarini, M. %A Shokat, K. M. %A García-Sastre, A. %A Rassen, J. A. %A Grosse, R. %A Rosenberg, O. S. %A Verba, K. A. %A Basler, C. F. %A Vignuzzi, M. %A Peden, A. A. %A Beltrao, P. %A Krogan, N. J. %A Consortium, QCRG Structural Biology %A Consortium, Zoonomia %D 2020 %T Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms %B Science %V 370 %N 6521 %7 2020/10/15 %8 12 %! Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms %@ 1095-9203 %R 10.1126/science.abe9403 %2 PMCID7808408 %M 33060197 %L 409 %F 409 %K COVID-19 Conserved Sequence Coronavirus Nucleocapsid Proteins Cryoelectron Microscopy Host Microbial Interactions Humans Mitochondrial Membrane Transport Proteins Phosphoproteins Protein Conformation Protein Interaction Maps SARS Virus SARS-CoV-2 Severe Acute Respiratory Syndrome %X The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo-electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study. %U https://salilab.org/pdf/Gordon_Science_2020.pdf %G eng %0 Journal Article %A Gordon, D. E. %A Jang, G. M. %A Bouhaddou, M. %A Xu, J. %A Obernier, K. %A O’Meara, M. J. %A Guo, J. Z. %A Swaney, D. L. %A Tummino, T. A. %A Hüttenhain, R. %A Kaake, R. M. %A Richards, A. L. %A Tutuncuoglu, B. %A Foussard, H. %A Batra, J. %A Haas, K. %A Modak, M. %A Kim, M. %A Haas, P. %A Polacco, B. J. %A Braberg, H. %A Fabius, J. M. %A Eckhardt, M. %A Soucheray, M. %A Bennett, M. J. %A Cakir, M. %A McGregor, M. J %A Li, Q. %A Naing, Z. Z. C. %A Zhou, Y. %A Peng, S. %A Kirby, I. T. %A Melnyk, J. E. %A Chorba, J. S. %A Lou, K. %A Dai, S. A. %A Shen, W. %A Shi, Y. %A Zhang, Z. %A Barrio-Hernandez, I. %A Memon, D. %A Hernandez-Armenta, C. %A Mathy, C. J.P. %A Perica, T. %A Pilla, K. B. %A Ganesan, S. J. %A Saltzberg, D. J. %A Ramachandran, R. %A Liu, X. %A Rosenthal, S. B. %A Calviello, L. %A Venkataramanan, S. %A Lin, Y. %A Wankowicz, S. A. %A Bohn, M. %A Trenker, R. %A Young, J. M. %A Cavero, D. %A Hiatt, J. %A Roth, T. %A Rathore, U. %A Subramanian, A. %A Noack, J. %A Hubert, M. %A Roesch, F. %A Vallet, T. %A Meyer, B. %A White, K. M. %A Miorin, L. %A Agard, D. %A Emerman, M. %A Ruggero, D. %A García-Sastre, A. %A Jura, N. %A Zastrow, M. %A Taunton, J. %A Schwartz, O. %A Vignuzzi, M. %A d’Enfert, C. %A Mukherjee, S. %A Jacobson, M. %A Malik, H. S. %A Fujimori, D. G. %A Ideker, T. %A Craik, C. S. %A Floor, S. %A Fraser, J. S. %A Gross, J. %A Sali, A. %A Kortemme, T. %A Beltrao, P. %A Shokat, K. %A Shoichet, B. K. %A Krogan, N. J. %D 2020 %T A SARS-CoV-2 protein interaction map reveals targets for drug repurposing %B Nature %V 583 %N 7816 %P 459-468 %! A SARS-CoV-2 protein interaction map reveals targets for drug repurposing %R 10.1101/2020.03.22.002386 %2 PMCID7431030 %M 32353859 %L 402 %F 402 %X An outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.HC-PPIsHigh confidence protein-protein interactionsPPIsprotein-protein interactionAP-MSaffinity purification-mass spectrometryCOVID-19Coronavirus Disease-2019ACE2angiotensin converting enzyme 2Orfopen reading frameNsp3papain-like proteaseNsp5main proteaseNspnonstructural proteinTPMtranscripts per million %U https://www.biorxiv.org/content/biorxiv/early/2020/03/22/2020.03.22.002386.full.pdf %0 Journal Article %A Gradisar, H. %A Bozic, S. %A Doles, T. %A Vengust, D. %A Hafner-Bratkovic, I. %A Mertelj, A. %A Webb, B. %A Sali, A. %A Klavzar, S. %A Jerala, R. %D 2013 %T Design of a single-chain polypeptide tetrahedron assembled from coiled-coil segments %B Nat Chem Biol %V 9 %P 362-366 %! Design of a single-chain polypeptide tetrahedron assembled from coiled-coil segments %2 PMCID3661711 %M 23624438;PMCID:PMC3661711 %L 293 %F 293 %U http://salilab.org/pdf/Gradisar_NatChemBiol_2013.pdf %0 Journal Article %A Graslund, S. %A Nordlund, P. %A Weigelt, J. %A Hallberg, B.M. %A Bray, J. %A Gileadi, O. %A Knapp, S. %A Oppermann, U. %A Arrowsmith, C. %A Hui, R. %A Ming, J. %A dhe-Paganon, S. %A Park, H. %A Savchenko, A. %A Yee, A. %A Edwards, A. %A Vincentelli, R. %A Cambillau, C. %A Kim, R. %A Kim, S. %A Rao, Z. %A Shi, Y. %A Terwilliger, T.C. %A Kim, C. %A Hung, L. %A Waldo, G.S. %A Peleg, Y. %A Albeck, S. %A Unger, T. %A Dym, O. %A Prilusky, J. %A Sussman, J.L. %A Stevens, R.C. %A Lesley, S.A. %A Wilson, I.A. %A Joachimiak, A. %A Collart, F. %A Dementieva, I. %A Donnelly, M.I. %A Eschenfeldt, W.H. %A Kim, Y. %A Stols, L. %A Wu, R. %A Zhou, M. %A Burley, S.K. %A Emtage, J.S. %A Sauder, J.M. %A Thompson, D. %A Bain, K. %A Luz, J. %A Gheyi, T. %A Zhang, F. %A Atwell, S. %A Almo, S.C. %A Bonanno, J.B. %A Fiser, A. %A Swaminathan, S. %A Studier, F.W. %A Chance, M.R. %A Sali, A. %A Acton, T.B. %A Xiao, R. %A Zhao, L. %A Ma, L.C. %A Hunt, J.F. %A Tong, L. %A Cunningham, K. %A Inouye, M. %A Anderson, S. %A Janjua, H. %A Shastry, R. %A Ho, C.K. %A Wang, D. %A Wang, H. %A Jiang, M. %A Montelione, G.T. %A Stuart, D.I. %A Owens, R.J. %A Daenke, S. %A Schutz, A. %A Heinemann, U. %A Yokoyama, S. %A Bussow, K. %A Gunsalus, K.C. %D 2008 %T Protein production and purification (vol 5, pg 135, 2008) %B Nat Methods %V 5 %N 4 %P 369-369 %8 Apr %! Protein production and purification (vol 5, pg 135, 2008) %@ 1548-7091 %R 10.1038/nmeth0408-369 %2 PMCID3178102 %M 18235434;PMCID:PMC3178102 %L 211 %F 211 %Z Hallberg, B. Martin %U http://salilab.org/pdf/Graslund_NatMethods_2008.pdf %0 Journal Article %A Greenberg, C. H. %A Kollman, J. %A Zelter, A. %A Johnson, R. %A MacCoss, M. J. %A Davis, T. N. %A Agard, D. A. %A Sali, A. %D 2016 %T Structure of γ-tubulin small complex based on a cryo-EM map, chemical cross-links, and a remotely related structure %B J Struct Biol %V 194 %N 3 %P 303-10 %7 2016/03/08 %8 Jun %! Structure of γ-tubulin small complex based on a cryo-EM map, chemical cross-links, and a remotely related structure %@ 1095-8657 %R 10.1016/j.jsb.2016.03.006 %2 PMCID4866596 %M 26968363 %L 361 %F 361 %X Modeling protein complex structures based on distantly related homologues can be challenging due to poor sequence and structure conservation. Therefore, utilizing even low-resolution experimental data can significantly increase model precision and accuracy. Here, we present models of the two key functional states of the yeast γ-tubulin small complex (γTuSC): one for the low-activity "open" state and another for the higher-activity "closed" state. Both models were computed based on remotely related template structures and cryo-EM density maps at 6.9Å and 8.0Å resolution, respectively. For each state, extensive sampling of alignments and conformations was guided by the fit to the corresponding cryo-EM density map. The resulting good-scoring models formed a tightly clustered ensemble of conformations in most regions. We found significant structural differences between the two states, primarily in the γ-tubulin subunit regions where the microtubule binds. We also report a set of chemical cross-links that were found to be consistent with equilibrium between the open and closed states. The protocols developed here have been incorporated into our open-source Integrative Modeling Platform (IMP) software package (http://integrativemodeling.org), and can therefore be applied to many other systems. %U https://salilab.org/pdf/Greenberg_JStructBiol_2016.pdf %G eng %0 Journal Article %A Groft, C. M. %A Beckmann, R. %A Sali, A. %A Burley, S. K. %D 2000 %T Crystal structures of ribosome anti-association factor IF6 %B Nat Struct Biol %V 7 %P 1156-1164 %! Crystal structures of ribosome anti-association factor IF6 %@ 1072-8368 %M 11101899 %L 90 %F 90 %X Ribosome anti-association factor elF6 (originally named according to translation initiation terminology as eukaryotic initiation factor 6) binds to the large ribosomal subunit, thereby preventing inappropriate interactions with the small subunit during initiation of protein synthesis. We have determined the X-ray structures of two IF6 homologs, Methanococcus jannaschii archaeal alF6 and Sacchromyces cerevisiae elF6, revealing a phylogenetically conserved 25 kDa protein consisting of five quasi identical alpha/beta subdomains arrayed about a five-fold axis of pseudosymmetry. Yeast elF6 prevents ribosomal subunit association. Comparative protein structure modeling with other known archaeal and eukaryotic homologs demonstrated the presence of two conserved surface regions, one or both of which may bind the large ribosomal subunit. %U http://salilab.org/pdf/Groft_NatStructBiol_2000.pdf %0 Journal Article %A Groft, C.M. %A Beckmann, R. %A Sali, A. %A Burley, S.K. %D 2001 %T Response to Paoli %B Nat Struct Biol %V 8 %P 745 %8 2001/// %! Response to Paoli %L 102 %F 102 %Z TY - JOUR %U http://salilab.org/pdf/Groft_NatStructBiol_2001.pdf %0 Journal Article %A Gruswitz, F %A Chaudhary, S %A Ho, J %A Schlessinger, A %A Bobak, P %A Ho, C %A Sali, A %A Westhoff, C %A Stroud, R %D 2010 %T Function of Human Rh based on Structure of RhCG at 2.1 Å %B Proc Natl Acad Sci U S A %V 107 %N 21 %P 9638-9643 %! Renal Ammonia Transport and the Structure of the Human Rhesus Glycoprotein RhCG at 2.1 A %2 PMCID2906887 %M 20457942;PMCID:PMC2906887 %L 237 %F 237 %U http://salilab.org/pdf/Gruswitz_ProcNatlAcadSciUSA_2010.pdf %0 Journal Article %A Guenther, B. %A Onrust, R. %A Sali, A. %A O'Donnell, M. %A Kuriyan, J. %D 1997 %T Crystal structure of the delta' subunit of the clamp-loader complex of E. coli DNA polymerase III %B Cell %V 91 %N 3 %P 335-345 %8 Oct 31 %! Crystal structure of the delta' subunit of the clamp-loader complex of E. coli DNA polymerase III %M 9363942 %L 59 %F 59 %K Adenosine Triphosphate/metabolism Amino Acid Sequence Crystallography, X-Ray DNA Polymerase III/*chemistry/metabolism Escherichia coli/*enzymology Hydrolysis Models, Molecular Molecular Sequence Data Phosphates/metabolism Protein Conformation Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid %X The crystal structure of the delta' subunit of the clamp-loader complex of E. coli DNA polymerase III has been determined. Three consecutive domains in the structure are arranged in a C-shaped architecture. The N-terminal domain contains a nonfunctional nucleotide binding site. The catalytic component of the clamp-loader complex is the gamma subunit, which is homologous to delta'. A sequence-structure alignment suggests that nucleotides bind to gamma at an interdomain interface within the inner surface of the "C." The alignment is extended to other clamp-loader complexes and to the RuvB family of DNA helicases, and suggests that each of these is assembled from C-shaped components that can open and close the jaws of the "C" in response to ATP binding and hydrolysis. %Z 0092-8674 Journal Article %U http://salilab.org/pdf/Guenther_Cell_1997.pdf %+ The Rockefeller University, New York, New York 10021, USA. %0 Journal Article %A Guo, A %A Zhang, J %A He, B %A Li, A %A Sun, T %A Li, C %A Wang, J %A Tai, R %A Fan, J %A Sali, A %A Stevens, RC %A Jiang, H %D 2022 %T Quantitative, in situ visualization of intracellular insulin vesicles in pancreatic β cells %B Proc Natl Acad Sci USA %V 119 %6 32 %P e2202695119 %! Quantitative, in situ visualization of intracellular insulin vesicles in pancreatic β cells %R 10.1073/pnas.2202695119 %2 PMCID9371705 %M 35921440 %L 432 %F 432 %U https://salilab.org/pdf/Guo_ProcNatlAcadSciUSA_2022.pdf %0 Journal Article %A Gutierrez, C. %A Chemmama, I. E. %A Mao, H. %A Yu, C. %A Echeverria, I. %A Block, S. A. %A Rychnovsky, S. D. %A Zheng, N. %A Sali, A. %A Huang, L. %D 2020 %T Structural dynamics of the human COP9 signalosome revealed by cross-linking mass spectrometry and integrative modeling %B Proc Natl Acad Sci U S A %V 117 %N 8 %P 4088-4098 %7 2020/02/07 %8 Feb %! Structural dynamics of the human COP9 signalosome revealed by cross-linking mass spectrometry and integrative modeling %@ 1091-6490 %R 10.1073/pnas.1915542117 %2 PMCID7049115 %M 32034103 %L 400 %F 400 %K COP9 signalosome architectures of protein complexes cross-linking mass spectrometry integrative structure modeling structural dynamics %X The COP9 signalosome (CSN) is an evolutionarily conserved eight-subunit (CSN1-8) protein complex that controls protein ubiquitination by deneddylating Cullin-RING E3 ligases (CRLs). The activation and function of CSN hinges on its structural dynamics, which has been challenging to decipher by conventional tools. Here, we have developed a multichemistry cross-linking mass spectrometry approach enabled by three mass spectometry-cleavable cross-linkers to generate highly reliable cross-link data. We applied this approach with integrative structure modeling to determine the interaction and structural dynamics of CSN with the recently discovered ninth subunit, CSN9, in solution. Our results determined the localization of CSN9 binding sites and revealed CSN9-dependent structural changes of CSN. Together with biochemical analysis, we propose a structural model in which CSN9 binding triggers CSN to adopt a configuration that facilitates CSN-CRL interactions, thereby augmenting CSN deneddylase activity. Our integrative structure analysis workflow can be generalized to define in-solution architectures of dynamic protein complexes that remain inaccessible to other approaches. %U https://salilab.org/pdf/Gutierrez_ProcNatlAcadSciUSA_2020.pdf %G eng %0 Journal Article %A Gutin, A. %A Sali, A. %A Abkevich, V. %A Karplus, M. %A Shakhnovich, E. %D 1998 %T Temperature dependence of the folding rate in a simple protein model: Search for a glass transition %B J Chem Phys %V 108 %P 6466-6483 %8 1998/// %! Temperature dependence of the folding rate in a simple protein model: Search for a glass transition %L 65 %F 65 %K Temperature %Z TY - JOUR %U http://salilab.org/pdf/Gutin_JChemPhys_1998.pdf %0 Journal Article %A Guttman, M. %A Weinkam, P. %A Sali, A. %A Lee, K. %D 2013 %T All-atom ensemble modeling to analyze small angle X-ray scattering of glycosylated proteins %B Structure %V 21 %P 321-331 %! All-atom ensemble modeling to analyze small angle X-ray scattering of glycosylated proteins %2 PMCID3840220 %M 23473666;PMCID:PMC3840220 %L 298 %F 298 %U http://salilab.org/pdf/Guttman_Structure_2013.pdf %0 Journal Article %A Guy, AJ %A Irani, V %A Beeson, JG %A Webb, B %A Sali, A %A Richards, JS %A Ramsland, PA %D 2018 %T Proteome-wide mapping of immune features onto Plasmodium protein three-dimensional structures %B Sci Rep %V 8 %N 1 %P 4355 %! Proteome-wide mapping of immune features onto Plasmodium protein three-dimensional structures %R 10.1038/s41598-018-22592-3 %2 PMCID5847524 %M 29531293 %L 383 %F 383 %U https://salilab.org/pdf/Guy_SciRep_2018.pdf %0 Journal Article %A Hancock, M. %A Peulen, T. O. %A Webb, B. %A Poon, B. %A Fraser, J. S. %A Adams, P. %A Sali, A. %D 2022 %T Integration of software tools for integrative modeling of biomolecular systems %B J Struct Biol %V 214 %N 1 %P 107841 %7 20220208 %8 Feb 08 %! Integration of software tools for integrative modeling of biomolecular systems %@ 1095-8657 %R 10.1016/j.jsb.2022.107841 %1 Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. %2 PMCID9278553 %M 35149213 %L 431 %F 431 %K integrative modeling integrative structural biology software integration structural modeling %X Integrative modeling computes a model based on varied types of input information, be it from experiments or prior models. Often, a type of input information will be best handled by a specific modeling software package. In such a case, we desire to integrate our integrative modeling software package, Integrative Modeling Platform (IMP), with software specialized to the computational demands of the modeling problem at hand. After several attempts, however, we have concluded that even in collaboration with the software's developers, integration is either impractical or impossible. The reasons for the intractability of integration include software incompatibilities, differing modeling logic, the costs of collaboration, and academic incentives. In the integrative modeling software ecosystem, several large modeling packages exist with often redundant tools. We reason, therefore, that the other development groups have similarly concluded that the benefit of integration does not justify the cost. As a result, modelers are often restricted to the set of tools within a single software package. The inability to integrate tools from distinct software negatively impacts the quality of the models and the efficiency of the modeling. As the complexity of modeling problems grows, we seek to galvanize developers and modelers to consider the long-term benefit that software interoperability yields. In this article, we formulate a demonstrative set of software standards for implementing a model search using tools from independent software packages and discuss our efforts to integrate the IMP and the crystallography suite Phenix within the Bayesian modeling framework. %Z Hancock, Matthew Peulen, Thomas-Otavio Webb, Ben Poon, Billy Fraser, James S Adams, Paul Sali, Andrej 2022/2/13 %U https://salilab.org/pdf/Hancock_JStructBiol_2022.pdf %+ Biophysics Graduate Program, University of California, San Francisco, MC 2240 1600 16th St, San Francisco, California 94143, United States Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, California 94158, United States. Electronic address: matthew.hancock@ucf.edu. Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, California 94158, United States. Electronic address: thomas-otavio.peulen@ucsf.edu. Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, California 94158, United States. Electronic address: ben@salilab.org. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Building 33 1 Cyclotron Rd, Berkeley, California 94270, United States. Electronic address: BKPoon@lbl.gov. Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, California 94158, United States Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, California, United States. Electronic address: jfraser@fraserlab.com. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Building 33 1 Cyclotron Rd, Berkeley, California 94270, United States Department of Bioengineering, University of California, Berkeley, MC 1762 306 Stanley Hall, Berkeley, California 94720, United States. Electronic address: PDAdams@lbl.gov. Department of Bioengineering and Therapeutic Sciences, UCSF Box 0775 1700 4th St, University of California, San Francisco, San Francisco, California 94158, United States Department of Pharmaceutical Chemistry, University of California, San Francisco, UCSF Box 2880 600 16th St, San Francisco, California 94143, United States Quantitative Biosciences Institute (QBI), University of California, San Francisco, 1700 4th St, San Francisco, California, United States. Electronic address: sali@salilab.org. %G eng %0 Journal Article %A Hanke, C.A. %A Westbrook, J.D. %A Webb, B.M. %A Peulen, T. %A Lawson, C.L. %A Sali, A. %A Berman, H.M. %A Seidel, C.A.M. %A Vallat, B. %D 2024 %T Making fluorescence-based integrative structures and associated kinetic information accessible %B Nat Meth, online ahead of print %! Making fluorescence-based integrative structures and associated kinetic information accessible %R 10.1038/s41592-024-02428-x %M 39349602 %L 458 %F 458 %0 Journal Article %A Hays, FA %A Roe-Zurz, Z %A Li, M %A Kelly, L %A Gruswitz, F %A Sali, A %A Stroud, RM %D 2009 %T Ratiocinative screen of eukaryotic integral membrane protein expression and solubilization for structure determination %B J Struct Funct Genom %V 10 %N 1 %P 9-16 %8 Mar %! Ratiocinative screen of eukaryotic integral membrane protein expression and solubilization for structure determination. %@ 1345-711X %2 PMCID2756966 %M 19031011;PMCID:PMC2756966 %L 215 %F 215 %X Persistent hurdles impede the successful determination of high-resolution crystal structures of eukaryotic integral membrane proteins (IMP). We designed a high-throughput structural genomics oriented pipeline that seeks to minimize effort in uncovering high-quality, responsive non-redundant targets for crystallization. This "discovery-oriented" pipeline sidesteps two significant bottlenecks in the IMP structure determination pipeline: expression and membrane extraction with detergent. In addition, proteins that enter the pipeline are then rapidly vetted by their presence in the included volume on a size-exclusion column-a hallmark of well-behaved IMP targets. A screen of 384 rationally selected eukaryotic IMPs in baker's yeast Saccharomyces cerevisiae is outlined to demonstrate the results expected when applying this discovery-oriented pipeline to whole-organism membrane proteomes. %U http://salilab.org/pdf/Hays_JStructFunctGenom_2009.pdf %+ Department of Biochemistry and Biophysics, University of California at San Francisco, San Francisco, CA, 94158-2517, USA, haysf@msg.ucsf.edu. %G eng %0 Journal Article %A Henderson, R. %A Sali, A. %A Baker, M. %A Carragher, B. %A Devkota, B. %A Downing, K. %A Egelman, E. %A Feng, Z. %A Frank, J. %A Grigorieff, N. %A Jiang, W. %A Ludtke, S. %A Medalia, O. %A Penczek, P. %A Rosenthal, P. %A Rossmann, M. %A Schmid, M. %A Schroder, G. %A Steven, A. %A Stokes, D. %A Westbrook, J. %A Wriggers, W. %A Yang, H. %A Young, J. %A Berman, H. %A Chiu, W. %A Kleywegt, G. %A Lawson, C. %D 2012 %T Outcome of the First Electron Microscopy Validation Task Force Meeting %B Structure %V 20 %P 205-214 %! Outcome of the First Electron Microscopy Validation Task Force Meeting %2 PMCID3328769 %M 22325770;PMCID:PMC3328769 %L 270 %F 270 %U http://salilab.org/pdf/Henderson_Structure_2012.pdf %0 Journal Article %A Hepburn, M. %A Saltzberg, D. J. %A Lee, L. %A Fang, S. %A Atkinson, C. %A Strynadka, N. C. J. %A Sali, A. %A Lees-Miller, S. P. %A Schriemer, D. C. %D 2021 %T The active DNA-PK holoenzyme occupies a tensed state in a staggered synaptic complex %B Structure %V 29 %P 1-12 %7 2020/12/30 %8 Dec %! The active DNA-PK holoenzyme occupies a tensed state in a staggered synaptic complex %@ 1878-4186 %R 10.1016/j.str.2020.12.006 %2 PMCID8675206 %M 33412091 %L 418 %F 418 %K DNA repair DNA-PK crosslinking mass spectrometry hydrogen/deuterium exchange modeling non-homologous end-joining structure synaptic complex %X In the non-homologous end-joining (NHEJ) of a DNA double-strand break, DNA ends are bound and protected by DNA-PK, which synapses across the break to tether the broken ends and initiate repair. There is little clarity surrounding the nature of the synaptic complex and the mechanism governing the transition to repair. We report an integrative structure of the synaptic complex at a precision of 13.5 Å, revealing a symmetric head-to-head arrangement with a large offset in the DNA ends and an extensive end-protection mechanism involving a previously uncharacterized plug domain. Hydrogen/deuterium exchange mass spectrometry identifies an allosteric pathway connecting DNA end-binding with the kinase domain that places DNA-PK under tension in the kinase-active state. We present a model for the transition from end-protection to repair, where the synaptic complex supports hierarchical processing of the ends and scaffold assembly, requiring displacement of the catalytic subunit and tension release through kinase activity. %U https://salilab.org/pdf/Hepburn_Structure_2021.pdf %G eng %0 Journal Article %A Hitchcock, D. %A Fan, H. %A Kim, J. %A Vetting, M. %A Hillerich, B. %A Seidel, R. %A Almo, S. %A Shoichet, B. %A Sali, A. %A Raushel, F. %D 2013 %T Structure-guided Discovery of New Deaminase Enzymes %B J Am Chem Soc %V 135 %P 13927-13933 %! Structure-guided Discovery of New Deaminase Enzymes %2 PMCID38227683 %M 23968233;PMCID:PMC38227683 %L 305 %F 305 %U http://salilab.org/pdf/Hitchcock_JAmChemSoc_2013.pdf %0 Journal Article %A Holliday, GL %A Akiva, E %A Meng, EC %A Brown, SD %A Calhoun, S %A Pieper, U %A Sali, A %A Booker, SJ %A Babbitt, PC %D 2018 %T Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug & Play” Domain %B Methods Enzymol %V 606 %P 1-71 %! Atlas of the Radical SAM Superfamily: Divergent Evolution of Function Using a “Plug & Play” Domain %R 10.1016/bs.mie. 2018 .06.004 %2 PMCID6445391 %M 30097089 %L 388 %F 388 %0 Journal Article %A Horst, J.A. %A Pieper, U. %A Sali, A. %A Zhan, L. %A Chopra, G. %A Samudrala, R. %A Featherstone, J.D.B. %D 2012 %T Strategic protein target analysis for developing drugs to stop dental caries %B Adv Dent Res %V 22 %P 86-93 %! Strategic protein target analysis for developing drugs to stop dental caries %2 PMCID3420364 %M 22899687;PMCID:PMC3420364 %L 281 %F 281 %U http://salilab.org/pdf/Horst_AdvDentRes_2012.pdf %0 Journal Article %A Huang, C. %A Morales, G. %A Vagi, A. %A Chanasyk, K. %A Ferrazzi, M. %A Burklow, C. %A Qiu, W. T. %A Feyfant, E. %A Sali, A. %A Stevens, R. L. %D 2000 %T Formation of enzymatically active, homotypic, and heterotypic tetramers of mouse mast cell tryptases. Dependence on a conserved Trp-rich domain on the surface %B J Biol Chem %V 275 %N 1 %P 351-358 %8 Jan 7 %! Formation of enzymatically active, homotypic, and heterotypic tetramers of mouse mast cell tryptases. Dependence on a conserved Trp-rich domain on the surface %M 10617625 %L 79 %F 79 %K Animals Circular Dichroism Enteropeptidase/metabolism Enzyme Activation/drug effects Glycerol/pharmacology Heparin/pharmacology Hydrogen-Ion Concentration Mast Cells/*enzymology Mice Protein Binding Protein Denaturation Protein Precursors/metabolism Protein Structure, Quaternary Recombinant Proteins/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Serine Endopeptidases/genetics/*metabolism %X Mouse mast cell protease (mMCP) 6 and mMCP-7 are homologous tryptases stored in granules as macromolecular complexes with heparin and/or chondroitin sulfate E containing serglycin proteoglycans. When pro-mMCP-7 and pseudozymogen forms of this tryptase and mMCP-6 were separately expressed in insect cells, all three recombinant proteins were secreted into the conditioned medium as properly folded, enzymatically inactive 33-kDa monomers. However, when their propeptides were removed, mMCP-6 and mMCP-7 became enzymatically active and spontaneously assumed an approximately 150-kDa tetramer structure. Heparin was not required for this structural change. When incubated at 37 degrees C, recombinant mMCP-7 progressively lost its enzymatic activity in a time-dependent manner. Its N-linked glycans helped regulate the thermal stability of mMCP-7. However, the ability of this tryptase to form the enzymatically active tetramer was more dependent on a highly conserved Trp-rich domain on its surface. Although recombinant mMCP-6 and mMCP-7 preferred to form homotypic tetramers, these tryptases readily formed heterotypic tetramers in vitro. This latter finding indicates that the tetramer structural unit is a novel way the mast cell uses to assemble varied combinations of tryptases. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Huang_JBiolChem_2000.pdf %+ Departments of Medicine, Boston, Massachusetts 02115, USA. %0 Journal Article %A Huang, C. %A Sali, A. %A Stevens, R. L. %D 1998 %T Regulation and function of mast cell proteases in inflammation %B J Clin Immunol %V 18 %N 3 %P 169-183 %8 May %! Regulation and function of mast cell proteases in inflammation %M 9624576 %L 63 %F 63 %K Animals Endopeptidases/*physiology Inflammation/*enzymology Mast Cells/*enzymology Mice Rats %Z 0271-9142 Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Huang_JClinImmunol_1998.pdf %+ Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA. %0 Journal Article %A Huang, C. %A Wong, G. W. %A Ghildyal, N. %A Gurish, M. F. %A Sali, A. %A Matsumoto, R. %A Qiu, W. T. %A Stevens, R. L. %D 1997 %T The tryptase, mouse mast cell protease 7, exhibits anticoagulant activity in vivo and in vitro due to its ability to degrade fibrinogen in the presence of the diverse array of protease inhibitors in plasma %B J Biol Chem %V 272 %N 50 %P 31885-31893 %8 Dec 12 %! The tryptase, mouse mast cell protease 7, exhibits anticoagulant activity in vivo and in vitro due to its ability to degrade fibrinogen in the presence of the diverse array of protease inhibitors in plasma %M 9395536 %L 53 %F 53 %K Anaphylaxis/blood Animals Anticoagulants/*metabolism Blood Proteins/chemistry/metabolism Enzyme Activation Fibrinogen/*metabolism Hydrogen-Ion Concentration Inflammation Mediators/*metabolism Mastocytosis/blood Mice Mutagenesis, Site-Directed Protease Inhibitors/*blood Rats Recombinant Proteins/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Serine Endopeptidases/genetics/*metabolism %X Mouse mast cell protease (mMCP) 7 is a tryptase of unknown function expressed by a subpopulation of mast cells that reside in numerous connective tissue sites. Because enzymatically active mMCP-7 is selectively released into the plasma of V3 mastocytosis mice undergoing passive systemic anaphylaxis, we used this in vivo model system to identify a physiologic substrate of the tryptase. Plasma samples taken from V3 mastocytosis mice that had been sensitized with immunoglobulin (Ig) E and challenged with antigen were found to contain substantial amounts of four 34-55-kDa peptides, all of which were derived from fibrinogen. To confirm the substrate specificity of mMCP-7, a pseudozymogen form of the recombinant tryptase was generated that could be activated after its purification. The resulting recombinant mMCP-7 exhibited potent anticoagulant activity in the presence of normal plasma and selectively cleaved the alpha-chain of fibrinogen to fragments of similar size as that seen in the plasma of the IgE/antigen-treated V3 mastocytosis mouse. Subsequent analysis of a tryptase-specific, phage display peptide library revealed that recombinant mMCP-7 preferentially cleaves an amino acid sequence that is nearly identical to that in the middle of the alpha-chain of rat fibrinogen. Because fibrinogen is a physiologic substrate of mMCP-7, this tryptase can regulate clot formation and fibrinogen/integrin-dependent cellular responses during mast cell-mediated inflammatory reactions. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Huang_JBiolChem_1997.pdf %+ Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA. %0 Journal Article %A Huang, F. %A Nguyen, T.T.T. %A Echeverria, I. %A Ramachandran, R. %A Cary, D.C. %A Paculova, H. %A Sali, A. %A Weiss, A. %A Peterlin, B.M. %A Fujinaga, K. %D 2021 %T Reversible phosphorylation of cyclin T1 promotes assembly and stability of P-TEFb %B eLife %V 10 %P e68473 %! Reversible phosphorylation of cyclin T1 promotes assembly and stability of P-TEFb %R 10.7554/eLife.68473 %2 PMCID8648303 %M 34821217 %L 424 %F 424 %U https://salilab.org/pdf/Huang_eLife_2021.pdf %0 Journal Article %A Hunt, J. E. %A Friend, D. S. %A Gurish, M. F. %A Feyfant, E. %A Sali, A. %A Huang, C. %A Ghildyal, N. %A Stechschulte, S. %A Austen, K. F. %A Stevens, R. L. %D 1997 %T Mouse mast cell protease 9, a novel member of the chromosome 14 family of serine proteases that is selectively expressed in uterine mast cells %B J Biol Chem %V 272 %N 46 %P 29158-29166 %8 Nov 14 %! Mouse mast cell protease 9, a novel member of the chromosome 14 family of serine proteases that is selectively expressed in uterine mast cells %M 9360993 %L 57 %F 57 %K Amino Acid Sequence Animals Base Sequence *Chromosome Mapping Cloning, Molecular Electrostatics Female Immunohistochemistry Mast Cells/*enzymology Mice Mice, Inbred BALB C Models, Molecular Molecular Sequence Data Protein Structure, Secondary RNA, Messenger/genetics Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid Serine Endopeptidases/chemistry/*genetics/metabolism Uterus/cytology/*enzymology %X Mouse mast cell protease (mMCP) 1, mMCP-2, mMCP-4, and mMCP-5 are members of a family of related serine proteases whose genes reside within an approximately 850 kilobase (kb) complex on chromosome 14 that does not readily undergo crossover events. While mapping the mMCP-1 gene, we isolated a novel gene that encodes a homologous serine protease designated mMCP-9. The mMCP-9 and mMCP-1 genes are only approximately 7 kb apart on the chromosome and are oriented back to back. The proximity of the mMCP-1 and mMCP-9 genes now suggests that the low recombination frequency of the complex is due to the closeness of some of its genes. The mMCP-9 transcript and protein were observed in the jejunal submucosa of Trichinella spiralis-infected BALB/c mice. However, in normal BALB/c mice, mMCP-9 transcript and protein were found only in those mast cells that reside in the uterus. Thus, the expression of mMCP-9 differs from that of all other chymases. The observation that BALB/c mouse bone marrow-derived mast cells developed with interleukin (IL) 10 and c-kit ligand contain mMCP-9 transcript, whereas those developed with IL-3 do not, indicates that the expression of this particular chymase is regulated by the cytokine microenvironment. Comparative protein structure modeling revealed that mMCP-9 is the only known granule protease with three positively charged regions on its surface. This property may allow mMCP-9 to form multimeric complexes with serglycin proteoglycans and other negatively charged proteins inside the granule. Although mMCP-9 exhibits a >50% overall amino acid sequence identity with its homologous chymases, it has a unique substrate-binding cleft. This finding suggests that each member of the chromosome 14 family of serine proteases evolved to degrade a distinct group of proteins. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Hunt_JBiolChem_1997.pdf %+ Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA. %0 Journal Article %A Ilyin, V. A. %A Pieper, U. %A Stuart, A. C. %A Marti-Renom, M. A. %A McMahan, L. %A Sali, A. %D 2003 %T ModView, visualization of multiple protein sequences and structures %B Bioinformatics %V 19 %N 1 %P 165-166 %! ModView, visualization of multiple protein sequences and structures %@ 1367-4803 %M 12499313 %L 117 %F 117 %X We describe ModView, a web application for visualization of multiple protein sequences and structures. ModView integrates a multiple structure viewer, a multiple sequence alignment editor, and a database querying engine. It is possible to interactively manipulate hundreds of proteins, to visualize conservative and variable residues, active and binding sites, fragments, and domains in protein families, as well as to display large macromolecular complexes such as ribosomes or viruses. As a Netscape plug-in, ModView can be included in HTML pages along with text and figures, which makes it useful for teaching and presentations. ModView is also suitable as a graphical interface to various databases because it can be controlled through JavaScript commands and called from CGI scripts. %U http://salilab.org/pdf/Ilyin_Bioinformatics_2003.pdf %0 Journal Article %A Iverson, G. M. %A Reddel, S. %A Victoria, E. J. %A Cockerill, K. A. %A Wang, Y. X. %A Marti-Renom, M. A. %A Sali, A. %A Marquis, D. M. %A Krilis, S. A. %A Linnik, M. D. %D 2002 %T Use of single point mutations in domain I of beta(2)-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies %B J Immunol %V 169 %N 12 %P 7097-7103 %! Use of single point mutations in domain I of beta(2)-glycoprotein I to determine fine antigenic specificity of antiphospholipid autoantibodies %@ 0022-1767 %M 12471146 %L 115 %F 115 %X Autoantibodies against beta(2)-glycoprotein I (beta(2)GPI) appear to be a critical feature of the antiphospholipid syndrome (APS). As determined using domain deletion mutants, human autoantibodies bind to the first of five domains present in beta(2)GPI. In this study the fine detail of the domain I epitope has been examined using 10 selected mutants of whole beta(2)GPI containing single point mutations in the first domain. The binding to beta(2)GPI was significantly affected by a number of single point mutations in domain I, particularly by mutations in the region of aa 40-43. Molecular modeling predicted these mutations to affect the surface shape and electrostatic charge of a facet of domain I. Mutation K19E also had an effect, albeit one less severe and involving fewer patients. Similar results were obtained in two different laboratories using affinity-purified anti-beta(2)GPI in a competitive inhibition ELISA and with whole serum in a direct binding ELISA. This study confirms that anti-beta(2)GPI autoantibodies bind to domain I, and that the charged surface patch defined by residues 40-43 contributes to a dominant target epitope. %U http://salilab.org/pdf/Iverson_JImmunol_2002.pdf %0 Book Section %A Jacobson, M. %A Sali, A. %D 2004 %T Comparative Protein Structure Modeling and Its Applications to Drug Discovery %E Overington, J. %B Annual Reports in Medicinal Chemistry %C London %I Inpharmatica Ltd. %V 39 %P 259-276 %! Comparative Protein Structure Modeling and Its Applications to Drug Discovery %L 138 %F 138 %U http://salilab.org/pdf/Jacobson_AnnRepMedChem_2004.pdf %0 Journal Article %A Jager, S. %A Cimermancic, P. %A Gulbahce, N. %A Johnson, J. %A McGovern, K. %A Clarke, S. %A Shales, M. %A Mercenne, G. %A Li, K. %A Barry, H. %A Jang, G. %A Akiva, E. %A Pache, L. %A Marlett, J. %A Roth, S. %A Stephens, M. %A D’Orso, I. %A Fernandes, J. %A Fahey, M. %A Mahon, C. %A O’Donoghue, A. %A Todorovic, A. %A Morris, J. %A Maltby, D. %A Alber, T. %A Cagney, G. %A Bushman, F. %A Young, J. %A Chanda, S. %A Sundquist, W. %A Kortemme, T. %A Hernandez, R. %A Craik, C. %A Burlingame, A. %A Sali, A. %A Frankel, A. %A Krogan, N. %D 2011 %T Global Landscape of HIV-Human Protein Complexes %B Nature %V 481 %P 365-370 %! Global Landscape of HIV-Human Protein Complexes %2 PMCID3310911 %M 22190034;PMCID:PMC3310911 %L 258 %F 258 %U http://salilab.org/pdf/Jager_Nature_2011a.pdf %W https://github.com/salilab/mist %0 Journal Article %A Jager, S. %A Kim, D.Y. %A Shindo, K. %A Kwon, E. %A LaRue, R. %A Mahon, C. %A Cimermancic, P. %A Yen, L. %A Stanley, D. %A Li, M. %A Burlingame, A. %A Sali, A. %A Craik, C. %A Harris, R. %A Gross, J. %A Krogan, N. %D 2011 %T Vif Hijacks CBFβ to Degrade APOBEC3G and Promote HIV-1 Infection %B Nature %V 481 %P 371-375 %! Vif Hijacks CBFβ to Degrade APOBEC3G and Promote HIV-1 Infection %2 PMCID3310910 %M 22190037;PMCID:PMC3310910 %L 259 %F 259 %U http://salilab.org/pdf/Jager_Nature_2011.pdf %0 Journal Article %A Jahangiri, A. %A Han, S. W. %A Sidorov, M. K. %A Chen, W. %A Rick, J. %A Schneidman-Duhovny, D. %A Mascharak, S. %A De Lay, M. %A Wagner, J. R. %A Imber, B. %A Flanigan, P. M. %A Chandra, A. %A Kuang, R. %A Castro, B. A. %A Lu, K. %A Bergers, G. %A Sali, A. %A Weiss, W. A. %A Aghi, M. K. %D 2017 %T Cross-activating c-Met/β1 integrin complex drives metastasis and invasive resistance in cancer %B Proc Natl Acad Sci USA %V 114 %N 1 %P E8685-E8694 %! Cross-activating c-Met/β1 integrin complex drives metastasis and invasive resistance in cancer %R 10.1073/pnas.1701821114 %2 PMCID5642678 %M 28973887 %L 364 %F 364 %U https://salilab.org/pdf/Jahangiri_PNAS_2017.pdf %0 Journal Article %A Jin, S. K. %A Martinek, S. %A Joo, W. S. %A Wortman, J. R. %A Mirkovic, N. %A Sali, A. %A Yandell, M. D. %A Pavletich, N. P. %A Young, M. W. %A Levine, A. J. %D 2000 %T Identification and characterization of a p53 homologue in Drosophila melanogaster %B Proc Natl Acad Sci U S A %V 97 %N 13 %P 7301-7306 %! Identification and characterization of a p53 homologue in Drosophila melanogaster %@ 0027-8424 %M 10860994 %L 87 %F 87 %X The tumor suppressor gene p53 in mammalian cells plays a critical role in safeguarding the integrity of genome. It functions as a sequence-specific transcription factor. Upon activation by a variety of cellular stresses, p53 transactivates downstream target genes, through which it regulates cell cycle and apoptosis. However, little is known about p53 in invertebrates. Here we report the identification and characterization of a Drosophila p53 homologue gene. dp53. dp53 encodes a 385-amino acid protein with significant homology to human p53 (hp53) in the region of the DNA-binding domain, and to a lesser extent the tetramerization domain. Purified dp53 DNA-binding domain protein was shown to bind to the consensus hp53-binding site by gel mobility analysis. In transient transfection assays, expression of dp53 in Schneider cells transcriptionally activated promoters that contained consensus hp53-responsive elements. Moreover, a mutant dp53 (Arg-155 to His-155), like its hp53 counterpart mutant, exerted a dominant-negative effect on transactivation. Ectopic expression of dp53 in Drosophila eye disk caused cell death and led to a rough eye phenotype. dp53 is expressed throughout the development of Drosophila with highest expression levels in early embryogenesis, which has a maternal component. Consistent with this, dp53 RNA levels were high in the nurse cells of the ovary. It appears that p53 is structurally and functionally conserved from flies to mammals. Drosophila will provide a useful genetic system to the further study of the p53 network. %U http://salilab.org/pdf/Jin_ProcNatlAcadSciUSA_2000.pdf %0 Journal Article %A Jishage, M. %A Yu, X. %A Shi, Y. %A Ganesan, S. J. %A Chen, W. Y. %A Sali, A. %A Chait, B. T. %A Asturias, F. J. %A Roeder, R. G. %D 2018 %T Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1 %B Nat Struct Mol Biol %V 25 %N 9 %P 859-867 %7 2018/09/06 %8 Sep %! Architecture of Pol II(G) and molecular mechanism of transcription regulation by Gdown1 %@ 1545-9985 %R 10.1038/s41594-018-0118-5 %2 PMCID6298426 %M 30190596 %L 391 %F 391 %X Tight binding of Gdown1 represses RNA polymerase II (Pol II) function in a manner that is reversed by Mediator, but the structural basis of these processes is unclear. Although Gdown1 is intrinsically disordered, its Pol II interacting domains were localized and shown to occlude transcription factor IIF (TFIIF) and transcription factor IIB (TFIIB) binding by perfect positioning on their Pol II interaction sites. Robust binding of Gdown1 to Pol II is established by cooperative interactions of a strong Pol II binding region and two weaker binding modulatory regions, thus providing a mechanism both for tight Pol II binding and transcription inhibition and for its reversal. In support of a physiological function for Gdown1 in transcription repression, Gdown1 co-localizes with Pol II in transcriptionally silent nuclei of early Drosophila embryos but re-localizes to the cytoplasm during zygotic genome activation. Our study reveals a self-inactivation through Gdown1 binding as a unique mode of repression in Pol II function. %U https://salilab.org/pdf/Jishage_NatStructMolBiol_2018.pdf %G eng %0 Journal Article %A John, B. %A Sali, A. %D 2003 %T Comparative protein structure modeling by iterative alignment, model building and model assessment %B Nucleic Acids Res %V 31 %N 14 %P 3982-3992 %! Comparative protein structure modeling by iterative alignment, model building and model assessment %@ 0305-1048 %R 10.1093/nar/gkg460 %M 12853614 %L 122 %F 122 %X Comparative or homology protein structure modeling is severely limited by errors in the alignment of a modeled sequence with related proteins of known three-dimensional structure. To ameliorate this problem, we have developed an automated method that optimizes both the alignment and the model implied by it. This task is achieved by a genetic algorithm protocol that starts with a set of initial alignments and then iterates through re-alignment, model building and model assessment to optimize a model assessment score. During this iterative process: (i) new alignments are constructed by application of a number of operators, such as alignment mutations and cross-overs; (ii) comparative models corresponding to these alignments are built by satisfaction of spatial restraints, as implemented in our program MODELLER; (iii) the models are assessed by a variety of criteria, partly depending on an atomic statistical potential. When testing the procedure on a very difficult set of 19 modeling targets sharing only 4-27% sequence identity with their template structures, the average final alignment accuracy increased from 37 to 45% relative to the initial alignment (the alignment accuracy was measured as the percentage of positions in the tested alignment that were identical to the reference structure-based alignment). Correspondingly, the average model accuracy increased from 43 to 54% (the model accuracy was measured as the percentage of the Calpha atoms of the model that were within 5 Angstrom of the corresponding Calpha atoms in the superposed native structure). The present method also compares favorably with two of the most successful previously described methods, PSI-BLAST and SAM. The accuracy of the final models would be increased further if a better method for ranking of the models were available. %U http://salilab.org/pdf/John_NucleicAcidsRes_2003.pdf %0 Journal Article %A John, B. %A Sali, A. %D 2004 %T Detection of homologous proteins by an intermediate sequence search %B Protein Sci %V 13 %N 1 %P 54-62 %8 Jan %! Detection of homologous proteins by an intermediate sequence search %M 14691221 %L 133 %F 133 %K Algorithms Amino Acid Sequence Comparative Study Computers Databases, Factual False Negative Reactions False Positive Reactions Molecular Sequence Data Protein Folding Protein Structure, Tertiary Proteins/*chemistry Reproducibility of Results Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sensitivity and Specificity Sequence Alignment/*methods Sequence Homology, Amino Acid Software %X We developed a variant of the intermediate sequence search method (ISS(new)) for detection and alignment of weakly similar pairs of protein sequences. ISS(new) relates two query sequences by an intermediate sequence that is potentially homologous to both queries. The improvement was achieved by a more robust overlap score for a match between the queries through an intermediate. The approach was benchmarked on a data set of 2369 sequences of known structure with insignificant sequence similarity to each other (BLAST E-value larger than 0.001); 2050 of these sequences had a related structure in the set. ISS(new) performed significantly better than both PSI-BLAST and a previously described intermediate sequence search method. PSI-BLAST could not detect correct homologs for 1619 of the 2369 sequences. In contrast, ISS(new) assigned a correct homolog as the top hit for 121 of these 1619 sequences, while incorrectly assigning homologs for only nine targets; it did not assign homologs for the remainder of the sequences. By estimate, ISS(new) may be able to assign the folds of domains in approximately 29,000 of the approximately 500,000 sequences unassigned by PSI-BLAST, with 90% specificity (1 - false positives fraction). In addition, we show that the 15 alignments with the most significant BLAST E-values include the nearly best alignments constructed by ISS(new). %Z 0961-8368 Journal Article %U http://salilab.org/pdf/John_ProteinSci_2004.pdf %+ Laboratory of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, New York, New York 10021, USA. %0 Journal Article %A Johnson, J. %A Santos, S. %A Pieper, U. %A Sali, A. %A Krogan, N. %A Beltrao, P. %D 2015 %T Prediction of functionally important phospho-regulatory events in Xenopus laevis oocytes %B PLOS Comp Bio %V 11 %N 8 %P e1004362 %! Prediction of functionally important phospho-regulatory events in Xenopus laevis oocytes %2 PMCID4552029 %M 26312481 %L 335 %F 335 %U http://salilab.org/pdf/Johnson_PLoSCompBio_2015.pdf %0 Journal Article %A Johnson, M. S. %A Sali, A. %A Blundell, T. L. %D 1990 %T Phylogenetic relationships from three-dimensional protein structures %B Methods Enzymol %V 183 %P 670-690 %! Phylogenetic relationships from three-dimensional protein structures %M 2156133 %L 16 %F 16 %K Amino Acid Sequence Animals Comparative Study Cytochrome c Group Globins Humans Mathematics *Models, Molecular Molecular Sequence Data Peptide Hydrolases *Phylogeny *Protein Conformation *Proteins Research Support, Non-U.S. Gov't Sequence Homology, Nucleic Acid %Z 0076-6879 Journal Article %U http://salilab.org/pdf/Johnson_MethodsEnzymol_1990.pdf %0 Book Section %A Johnson, M.S. %A Overington, J.P. %A Sali, A. %D 1990 %T Knowledge-based protein modelling: Human plasma kallikrein and human neutrophil defensin %E Vilafranca, J. J. %B Chemistry: Techniques Structure and Function %I Academic Press, Inc. %P 567-574 %! Knowledge-based protein modelling: Human plasma kallikrein and human neutrophil defensin %L 17 %F 17 %K Human chemistry %Z TY - CHAP %U http://salilab.org/pdf/Johnson_ChemTechStrucFunc_1990.pdf %+ San Diego, US %0 Book Section %A Johnson, M.S. %A Overington, J.P. %A Sali, A. %A Blundell, T.L. %D 1992 %T From the comparative analysis of proteins to similarity-based modelling %E Ratner, V. A. %E Kolchanov, N. A. %B Computer Modelling of Biomolecular Processes %I Nova Science Publishers %P 191-196 %! From the comparative analysis of proteins to similarity-based modelling %L 27 %F 27 %K analysis Proteins %Z TY - CHAP %U http://salilab.org/pdf/Johnson_CompModelBioProc_1992.pdf %+ 6080 Jericho Turnpike %0 Journal Article %A Johnson, M.S. %A Overington, J. %A Sali, A. %A Zhu, Z. %A Donnelly, D. %A Thomas, P. %A McLeod, A. %A Goold, R. %A Topham, C. %A Blundell, T.L. %D 1990 %T From comparative structure analysis to protein engineering: Knowledge-based protein modelling and design %B Fresenius Journal of Analytic Chemistry %V 337 %P 1-3 %8 1990/// %! From comparative structure analysis to protein engineering: Knowledge-based protein modelling and design %L 18 %F 18 %K analysis %Z TY - JOUR %U http://salilab.org/pdf/Johnson_FreseniusJournalofAnalyticChemistry_1990.pdf %0 Journal Article %A Kaake, R.M. %A Echeverria, I. %A Kim, S.J. %A Von Dollen, J. %A Chesarino, N.M. %A Feng, Y. %A Yu, C. %A Ta, H. %A Chelico, L. %A Huang, L. %A Gross, J. %A Andrej Sali, A. %A Krogan, N.J. %D 2021 %T Characterization of a A3G-VifHIV-1-CRL5-CBFβ structure using a cross-linking mass spectrometry pipeline for integrative modeling of host-pathogen complexes %B Mol Cell Prot %V 20 %P 100132 %! Characterization of a A3G-VifHIV-1-CRL5-CBFβ structure using a cross-linking mass spectrometry pipeline for integrative modeling of host-pathogen complexes %R 10.1016/j.mcpro.2021.100132 %2 PMCID8459920 %M 34389466 %L 422 %F 422 %U https://salilab.org/pdf/Kaake_MolCellProt_2021.pdf %0 Journal Article %A Kamat, S.S. %A Bagaria, A. %A Kumaran, D. %A Holmes-Hampton, G.P. %A Fan, H. %A Sali, A. %A Sauder, J.M. %A Burley, S.K. %A Lindahl, P.A. %A Swaminathan, S. %A Raushel, F.M. %D 2011 %T Catalytic Mechanism and Three-Dimensional Structure of Adenine Deaminase %B Biochemistry %V 50 %P 1917-1927 %! Catalytic Mechanism and Three-Dimensional Structure of Adenine Deaminase %2 PMCID3059353 %M 21247091;PMCID:PMC Journal- In Process %L 251 %F 251 %U http://salilab.org/pdf/Kamat_Biochemistry_2011.pdf %0 Journal Article %A Kamat, S.S. %A Fan, H. %A Sauder, J.M. %A Burley, S.K. %A Shoichet, B.K. %A Sali, A. %A Raushel, F.M. %D 2011 %T Enzymatic deamination of the epigenetic base N-6-methyladenine %B J Am Chem Soc %V 133 %P 2080-2083 %! Enzymatic deamination of the epigenetic base N-6-methyladenine %2 PMCID3043370 %M 21275375;PMCID:PMC3043370 %L 254 %F 254 %U http://salilab.org/pdf/Kamat_JAmChemSoc_2011a.pdf %0 Journal Article %A Kandiah, D. A. %A Sali, A. %A Sheng, Y. %A Victoria, E. J. %A Marquis, D. M. %A Coutts, S. M. %A Krilis, S. A. %D 1998 %T Current insights into the "antiphospholipid" syndrome: clinical, immunological, and molecular aspects %B Advanced Immunology Journal %V 70 %P 507-563 %! Current insights into the "antiphospholipid" syndrome: clinical, immunological, and molecular aspects %M 9755344 %L 64 %F 64 %K Antibodies, Antiphospholipid/immunology *Antiphospholipid Syndrome/genetics/immunology/physiopathology Female Glycoproteins/immunology Humans Lupus Coagulation Inhibitor/immunology Male Models, Molecular Pregnancy Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. %X Advances in defining the target antigen(s) for the autoantibodies in the APS highlight the inadequacies of the current classification of these autoantibodies into anticardiolipin and LA antibodies. The discovery that beta 2GPI is the target antigen for the autoantibodies detected in solid-phase immunoassays has opened a number of areas of research linking these autoantibodies to atherogenesis and thrombus formation. Although the role of beta 2GPI in the regulation of blood coagulation in unclear, current evidence suggests that anti-beta 2GPI antibodies interfere with its "normal" role and appear to promote a procoagulant tendency. The expansion of research in this area and the diversity of the clinical manifestations of patients with APS have resulted in the inclusion of molecular biologists and pharmaceutical companies joining immunologists, hematologists, rheumatologists, obstetricians, neurologists, vascular surgeons, and protein and lipid biochemists in attempting to understand the pathophysiology of this condition. Although the published literature may result in conflicting results and introduce new controversies, developing standardized laboratory methods and extrapolation of in vitro experimental results to the vivo situation will advance our understanding of the regulation of the immune system and its interaction with normal hemostatic mechanisms. Since the authors' last review in 1991, the study and understanding of the pathophysiology of APS have evolved from lipid biochemistry to molecular techniques that may eventually provide specific therapies for the clinical manifestations of this condition. Although current treatment has improved the morbidity associated with this condition, especially in improving pregnancy outcomes, future therapies, as outlined in this review, may specifically address the biological abnormalities and have fewer side effects. Better diagnostic tools, such as magnetic resonance imaging with perfusion studies, will allow the study of the true incidence and prevalence of vascular flow changes/tissue ischemia and infarction associated with aPL antibodies and help determine treatment and prophylaxis for APS patients. APS is still the only hypercoagulable condition where both arterial and venous beds can be affected independently or in the same individual. %Z 0065-2776 Journal Article Review %U http://salilab.org/pdf/Kandiah_AdvancedImmunologyJournal_1998.pdf %+ Department of Immunology, Allergy, and Infectious Disease, University of New South Wales School of Medicine, St. George Hospital, Kogarah, Australia. %0 Journal Article %A Karchin, R. %A Diekhans, M. %A Kelly, L. %A Thomas, D. J. %A Pieper, U. %A Eswar, N. %A Haussler, D. %A Sali, A. %D 2005 %T LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources %B Bioinformatics %V 21 %N 12 %P 2814-2820 %8 Jun 15 %! LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources %M 15827081 %L 148 %F 148 %X MOTIVATION: The NCBI dbSNP database lists over 9 million single nucleotide polymorphisms (SNPs) in the human genome, but currently contains limited annotation information. SNPs that result in amino acid residue changes (nsSNPs) are of critical importance in variation between individuals, including disease and drug sensitivity. RESULTS: We have developed LS-SNP, a genomic scale software pipeline to annotate nsSNPs. LS-SNP comprehensively maps nsSNPs onto protein sequences, functional pathways and comparative protein structure models, and predicts positions where nsSNPs destabilize proteins, interfere with the formation of domain-domain interfaces, have an effect on protein-ligand binding or severely impact human health. It currently annotates 28 043 validated SNPs that produce amino acid residue substitutions in human proteins from the SwissProt/TrEMBL database. Annotations can be viewed via a web interface either in the context of a genomic region or by selecting sets of SNPs, genes, proteins or pathways. These results are useful for identifying candidate functional SNPs within a gene, haplotype or pathway and in probing molecular mechanisms responsible for functional impacts of nsSNPs. AVAILABILITY: http://www.salilab.org/LS-SNP CONTACT: rachelk@salilab.org SUPPLEMENTARY INFORMATION: http://salilab.org/LS-SNP/supp-info.pdf. %Z 1367-4803 Journal Article %U http://salilab.org/pdf/Karchin_Bioinformatics_2005.pdf %+ Department of Biopharmaceutical Sciences, University of California at San Francisco San Francisco, CA 94143, USA. %0 Journal Article %A Karchin, R. %A Monteiro, A. N. %A Tavtigian, S. V. %A Carvalho, M. A. %A Sali, A. %D 2007 %T Functional Impact of Missense Variants in BRCA1 Predicted by Supervised Learning %B PLoS Computational Biology %V 3 %N 2 %P e26 %8 Feb 16 %! Functional Impact of Missense Variants in BRCA1 Predicted by Supervised Learning %M 17305420 %L 173 %F 173 %X Many individuals tested for inherited cancer susceptibility at the BRCA1 gene locus are discovered to have variants of unknown clinical significance (UCVs). Most UCVs cause a single amino acid residue (missense) change in the BRCA1 protein. They can be biochemically assayed, but such evaluations are time-consuming and labor-intensive. Computational methods that classify and suggest explanations for UCV impact on protein function can complement functional tests. Here we describe a supervised learning approach to classification of BRCA1 UCVs. Using a novel combination of 16 predictive features, the algorithms were applied to retrospectively classify the impact of 36 BRCA1 C-terminal (BRCT) domain UCVs biochemically assayed to measure transactivation function and to blindly classify 54 documented UCVs. Majority vote of three supervised learning algorithms is in agreement with the assay for more than 94% of the UCVs. Two UCVs found deleterious by both the assay and the classifiers reveal a previously uncharacterized putative binding site. Clinicians may soon be able to use computational classifiers such as those described here to better inform patients. These classifiers can be adapted to other cancer susceptibility genes and systematically applied to prioritize the growing number of potential causative loci and variants found by large-scale disease association studies. %Z 1553-7358 (Electronic) Journal article %U http://salilab.org/pdf/Karchin_PLoSComputationalBiology_2007.pdf %0 Journal Article %A Karchin, R %A Agarwal, M %A Sali, A %A Couch, F %A Beattie, MS %D 2008 %T Classifying Variants of Undetermined Significance in BRCA2 with Protein Likelihood Ratios %B Cancer Inform %V 6 %P 203-216 %! Classifying Variants of Undetermined Significance in BRCA2 with Protein Likelihood Ratios. %@ 1176-9351 %2 PMCID2587343 %M 19043619;PMCID:PMC2587343 %L 203 %F 203 %X BACKGROUND: Missense (aminoacid changing) variants found in cancer predisposition genes often create difficulties when clinically interpreting genetic testing results. Although bioinformatics has developed approaches to predicting the impact of these variants, many of these approaches have not been readily applicable in the clinical setting. Bioinformatics approaches for predicting the impact of these variants have not yet found their footing in clinical practice because 1) interpreting the medical relevance of predictive scores is difficult; 2) the relationship between bioinformatics "predictors" (sequence conservation, protein structure) and cancer susceptibility is not understood. METHODOLOGY/PRINCIPAL FINDINGS: We present a computational method that produces a probabilistic likelihood ratio predictive of whether a missense variant impairs protein function. We apply the method to a tumor suppressor gene, BRCA2, whose loss of function is important to cancer susceptibility. Protein likelihood ratios are computed for 229 unclassified variants found in individuals from high-risk breast/ovarian cancer families. We map the variants onto a protein structure model, and suggest that a cluster of predicted deleterious variants in the BRCA2 OB1 domain may destabilize BRCA2 and a protein binding partner, the small acidic protein DSS1. We compare our predictions with variant "re-classifications" provided by Myriad Genetics, a biotechnology company that holds the patent on BRCA2 genetic testing in the U.S., and with classifications made by an established medical genetics model [1]. Our approach uses bioinformatics data that is independent of these genetics-based classifications and yet shows significant agreement with them. Preliminary results indicate that our method is less likely to make false positive errors than other bioinformatics methods, which were designed to predict the impact of missense mutations in general. CONCLUSIONS/SIGNIFICANCE: Missense mutations are the most common disease-producing genetic variants. We present a fast, scalable bioinformatics method that integrates information about protein sequence, conservation, and structure in a likelihood ratio that can be integrated with medical genetics likelihood ratios. The protein likelihood ratio, together with medical genetics likelihood ratios, can be used by clinicians and counselors to communicate the relevance of a VUS to the individual who has that VUS. The approach described here is generalizable to regions of any tumor suppressor gene that have been structurally determined by X-ray crystallography or for which a protein homology model can be built. %Z PMC2587343 %U http://salilab.org/pdf/Karchin_CancerInformatics_2008.pdf %+ Department of Biomedical Engineering, Institute for Computational Medicine, Johns Hopkins University. %G eng %0 Journal Article %A Karchin, R %A Kelly, L %A Sali, A %D 2005 %T Improving functional annotation of non-synonomous SNPs with information theory %B Pac Symp Biocomput %P 397-408 %! Improving functional annotation of non-synonomous SNPs with information theory. %@ 1793-5091 %M 15759645 %L 144 %F 144 %K Analysis of Variance Bacteriophage T4 Base Sequence Databases, Nucleic Acid Evolution Markov Chains Models, Genetic Mutation Polymorphism, Single Nucleotide %X Automated functional annotation of nsSNPs requires that amino-acid residue changes are represented by a set of descriptive features, such as evolutionary conservation, side-chain volume change, effect on ligand-binding, and residue structural rigidity. Identifying the most informative combinations of features is critical to the success of a computational prediction method. We rank 32 features according to their mutual information with functional effects of amino-acid substitutions, as measured by in vivo assays. In addition, we use a greedy algorithm to identify a subset of highly informative features. The method is simple to implement and provides a quantitative measure for selecting the best predictive features given a set of features that a human expert believes to be informative. We demonstrate the usefulness of the selected highly informative features by cross-validated tests of a computational classifier, a support vector machine (SVM). The SVM's classification accuracy is highly correlated with the ranking of the input features by their mutual information. Two features describing the solvent accessibility of "wild-type" and "mutant" amino-acid residues and one evolutionary feature based on superfamily-level multiple alignments produce comparable overall accuracy and 6% fewer false positives than a 32-feature set that considers physiochemical properties of amino acids, protein electrostatics, amino-acid residue flexibility, and binding interactions. %U http://salilab.org/pdf/Karchin_PacSympBiocomput_2005.pdf %+ Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143-2240, USA. %G eng %0 Book Section %A Karplus, M. %A Caflisch, A. %A Sali, A. %A Shakhnovich, E. %D 1995 %T Protein dynamics: From the native to the unfolded state and back again %E al, A. Pullman et %B Modelling of Biomolecular Structures and Mechanisms %I Kluwer Academic Publishers %P 69-84 %! Protein dynamics: From the native to the unfolded state and back again %L 37 %F 37 %Z TY - CHAP %U http://salilab.org/pdf/Karplus_ModBioStrMech_1995.pdf %+ Dordrecht, Netherlands %0 Journal Article %A Karplus, M. %A Sali, A. %D 1995 %T Theoretical studies of protein folding and unfolding %B Curr Opin Struct Biol %V 5 %N 1 %P 58-73 %8 Feb %! Theoretical studies of protein folding and unfolding %M 7773748 %L 36 %F 36 %K Models, Theoretical *Protein Folding Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. %X The mechanism of protein folding is being investigated theoretically by the use of both simplified and all-atom models of the polypeptide chain. Lattice heteropolymer simulations of the folding process have led to proposals for the folding mechanism and for the resolution of the Levinthal paradox. Both stability and rapid folding have been shown in model studies to result from the presence of a pronounced global energy minimum corresponding to the native state. Concomitantly, molecular dynamics simulations with detailed atomic models have been used to analyze the initial stages of protein unfolding. Results concerning possible folding intermediates and the role of water in the unfolding process have been obtained. The two types of theoretical approaches are providing information essential for an understanding of the mechanism of protein folding and are useful for the design of experiments to study the mechanism in different proteins. %Z 0959-440x Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Karplus_CurrOpinStructBiol_1995.pdf %+ Universite Louis Pasteur, Strasbourg, France. %0 Journal Article %A Karplus, M. %A Sali, A. %A Shakhnovich, E. %D 1995 %T Kinetics of protein folding %B Nature %V 373 %P 664-665 %8 1995/// %! Kinetics of protein folding %L 46 %F 46 %K Kinetics Protein Folding %Z TY - JOUR %U http://salilab.org/pdf/Karplus_Nature_1995.pdf %0 Journal Article %A Kelly, L. %A Fukushima, H. %A Karchin, R. %A Gow, J.M. %A Chinn, L.W. %A Pieper, U. %A Segal, M.R. %A Kroetz, D.L. %A Sali, A. %D 2011 %T Response to "Predictable difficulty or difficulty to predict" by Tamas Aranyi, Krisztina Fulop, Orsolya Symmons, Viola Pomozi, and Andras Varadi %B Protein Sci %V 20 %P 4-5 %! Response to "Predictable difficulty or difficulty to predict" by Tamas Aranyi, Krisztina Fulop, Orsolya Symmons, Viola Pomozi, and Andras Varadi %L 249 %F 249 %U http://salilab.org/pdf/Kelly_ProteinSci_2010a.pdf %0 Journal Article %A Kelly, L. %A Pieper, U. %A Eswar, N. %A Hays, F. A. %A Li, M. %A Roe-Zurz, Z. %A Kroetz, D. %A Giacomini, K. M. %A Stroud, R. M. %A Sali, A. %D 2009 %T A survey of integral alpha-helical membrane proteins %B J Struct Funct Genomics %V 10 %P 269-280 %! A taxonomic profile of the membrane protein universe %@ 1345-711X %2 PMCID2780624 %M 19760129;PMCID:PMC2780624 %L 229 %F 229 %U http://salilab.org/pdf/Kelly_JStructFunctGenom_2009.pdf %0 Journal Article %A Kelly, L %A Fukushima, H %A Karchin, R %A Gow, JM %A Chinn, LW %A Pieper, U %A Segal, MR %A Kroetz, DL %A Sali, A %D 2010 %T Functional Hot Spots in Human ATP-binding Cassette Transporter Nucleotide Binding Domains %B Protein Sci %V 19 %P 2110-2121 %! Functional Hot Spots in Human ATP-binding Cassette Transporter Nucleotide Binding Domains %2 PMCID3005782 %M 20799350;PMCID:PMC3005782 %L 246 %F 246 %U http://salilab.org/pdf/Kelly_ProteinSci_2010.pdf %0 Journal Article %A Kelly, L %A Karchin, R %A Sali, A %D 2007 %T Protein interactions and disease phenotypes in the ABC transporter superfamily %B Pac Symp Biocomput %P 51-63 %! Protein interactions and disease phenotypes in the ABC transporter superfamily. %@ 1793-5091 %M 17990484 %L 174 %F 174 %K ATP-Binding Cassette Transporters Binding Sites Computational Biology Computer Simulation Conserved Sequence Databases, Protein Evolution, Molecular Humans Models, Molecular Mutation Phenotype Protein Interaction Mapping Sequence Alignment %X ABC transporter proteins couple the energy of ATP binding and hydrolysis to substrate transport across a membrane. In humans, clinical studies have implicated mutations in 19 of the 48 known ABC transporters in diseases such as cystic fibrosis and adrenoleukodystrophy. Although divergent in sequence space, the overall topology of these proteins, consisting of two transmembrane domains and two ATP-binding cassettes, is likely to be conserved across diverse organisms. We examine known intra-transporter domain interfaces using crystallographic structures of isolated and complexed domains in ABC transporter proteins and find that the nucleotide binding domain interfaces are better conserved than interfaces at the transmembrane domains. We then apply this analysis to identify known disease-associated point and deletion mutants for which disruption of domain-domain interfaces might indicate the mechanism of disease. Finally, we suggest a possible interaction site based on conservation of sequence and disease-association of point mutants. %U http://salilab.org/pdf/Kelly_PacSympBiocomput_2007.pdf %+ Program in Biological and Medical Informatics, Department of Biopharmaceutical Sciences, University of California at San Francisco, CA 94158, USA. libusha@salilab.org %G eng %0 Journal Article %A Ketaren, N.E. %A Mast, F.D. %A Fridy, P.C. %A Olivier, J.P. %A Sanyal, T. %A Sali, A. %A Chait, B.T. %A Rout, M.P. %A Aitchison, J.D. %D 2024 %T Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus %B eLife %V 12 %P RP89423 %! Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus %R 10.7554/eLife.89423 %2 PMCID11076045 %M 38712823 %L 450 %F 450 %U https://salilab.org/pdf/Ketaren_eLife_2024.pdf %0 Journal Article %A Khuri, N. %A Zur, A. %A Wittwer, M. %A Lin, L. %A Yee, S. W. %A Sali, A. %A Giacomini, K. %D 2017 %T Computational Discovery And Experimental Validation of Inhibitors of the Human Intestinal Transporter, OATP2B1 %B J Chem Inf Model %V 57 %N 6 %P 1402-1413 %! Computational Discovery And Experimental Validation of Inhibitors of the Human Intestinal Transporter, OATP2B1 %R 10.1021/acs.jcim.6b00720 %M 28562037 %L 366 %F 366 %U https://salilab.org/pdf/Khuri_JChemInfModel_2017.pdf %0 Journal Article %A Kim, S. K. %A Dickinson, M. S. %A Finer-Moore, J. %A Guan, Z. %A Kaake, R. M. %A Echeverria, I. %A Chen, J. %A Pulido, E. H. %A Sali, A. %A Krogan, N. J. %A Rosenberg, O. S. %A Stroud, R. M. %D 2023 %T Structure and dynamics of the essential endogenous mycobacterial polyketide synthase Pks13 %B Nat Struct Mol Biol %V 30 %N 3 %P 296-308 %7 20230213 %8 Mar %! Structure and dynamics of the essential endogenous mycobacterial polyketide synthase Pks13 %@ 1545-9985 %R 10.1038/s41594-022-00918-0 %2 PMCID10312659 %M 36782050 %L 444 %F 444 %K Polyketide Synthases Mycobacterium tuberculosis Mycolic Acids Fatty Acids %X The mycolic acid layer of the Mycobacterium tuberculosis cell wall is essential for viability and virulence, and the enzymes responsible for its synthesis are targets for antimycobacterial drug development. Polyketide synthase 13 (Pks13) is a module encoding several enzymatic and transport functions that carries out the condensation of two different long-chain fatty acids to produce mycolic acids. We determined structures by cryogenic-electron microscopy of dimeric multi-enzyme Pks13 purified from mycobacteria under normal growth conditions, captured with native substrates. Structures define the ketosynthase (KS), linker and acyl transferase (AT) domains at 1.8 Å resolution and two alternative locations of the N-terminal acyl carrier protein. These structures suggest intermediate states on the pathway for substrate delivery to the KS domain. Other domains, visible at lower resolution, are flexible relative to the KS-AT core. The chemical structures of three bound endogenous long-chain fatty acid substrates were determined by electrospray ionization mass spectrometry. %Z Kim, Sun Kyung Dickinson, Miles Sasha Finer-Moore, Janet Guan, Ziqiang Kaake, Robyn M Echeverria, Ignacia Chen, Jen Pulido, Ernst H Sali, Andrej Krogan, Nevan J Rosenberg, Oren S Stroud, Robert M 2023/2/15 %U https://salilab.org/pdf/Kim_NatStructMolBiol_2023.pdf %+ Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA. Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA, USA. Department of Biochemistry, Duke University Medical Center, Durham, NC, USA. Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, CA, USA. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA, USA. Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA, USA. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA. Oren.Rosenberg@ucsf.edu. Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, CA, USA. Oren.Rosenberg@ucsf.edu. Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA. stroud@msg.ucsf.edu. Chemistry and Chemical Biology Graduate Program, University of California San Francisco, San Francisco, CA, USA. stroud@msg.ucsf.edu. %G eng %0 Journal Article %A Kim, S.J. %A Fernandez-Martinez, J. %A Sampathkumar, P. %A Martel, A. %A Matsui, T. %A Tsuruta, H. %A Weiss, T. %A Markina-Inarrairaegui, A. %A Bonanno, J. %A Sauder, M. %A Burley, S. %A Chait, B.T. %A Almo, S. %A Rout, M. %A Sali, A. %D 2014 %T Integrative Structure-Function Mapping of the Nucleoporin Nup133 Suggests a Conserved Mechanism for Membrane Anchoring of the Nuclear Pore Complex %B Mol Cell Proteomics %V 13 %P 2911-2926 %! Integrative Structure-Function Mapping of the Nucleoporin Nup133 Suggests a Conserved Mechanism for Membrane Anchoring of the Nuclear Pore Complex %2 PMCID4223481 %M 25139911;PMCID:PMC4223481 %L 329 %F 329 %U http://salilab.org/pdf/Kim_MolCellProteomics_2014.pdf %0 Journal Article %A Kim, SJ %A Fernandez-Martinez, J %A Nudelman, I %A Shi, Y %A Zhang, W %A Raveh, B %A Herricks, T %A Slaughter, BD %A Hogan, J %A Upla, P %A Chemmama, IE %A Pellarin, R %A Echeverria, I %A Shivaraju, M %A Chaudhury, AS %A Wang, J %A Williams, R %A Unruh, JR %A Greenberg, CH %A Jacobs, EY %A Yu, Z %A de la Cruz, MJ %A Mironska, R %A Stokes, DL %A Aitchison, JD %A Jarrold, MF %A Gerton, JL %A Ludtke, SJ %A Akey, CW %A Chait, BT %A Sali, A %A Rout, MP %D 2018 %T Integrative Structure and Functional Anatomy of a Nuclear Pore Complex %B Nature %V 555 %N 7697 %P 475-482 %! Integrative Structure and Functional Anatomy of a Nuclear Pore Complex %R 10.1038/nature26003 %2 PMCID6022767 %M 29539637 %L 384 %F 384 %U https://salilab.org/pdf/Kim_Nature_2018.pdf %0 Journal Article %A Klammt, C. %A Maslennikov, I. %A Bayrhuber, M. %A Eichmann, C. %A Vajpai, N. %A Chiu, E. %A Blain, K. %A Esquivies, L. %A Kwon, H. %A Balana, B. %A Pieper, U. %A Sali, A. %A Slesinger, P. %A Kwiatkowski, W. %A Rick, R. %A Choe, S. %D 2012 %T Facile Backbone structure determination of human membrane proteins by NMR spectroscopy %B Nat Methods %V 8 %P 834-839 %! Facile Backbone structure determination of human membrane proteins by NMR spectroscopy %2 PMCID3723349 %M 22609626;PMCID:PMC3723349 %L 276 %F 276 %U http://salilab.org/pdf/Klammt_NatMethods_2012.pdf %0 Journal Article %A Koh, I. Y. Y. %A Eyrich, V. A. %A Marti-Renom, M. A. %A Przybylski, D. %A Madhusudhan, M. S. %A Eswar, N. %A Grana, O. %A Pazos, F. %A Valencia, A. %A Sali, A. %A Rost, B. %D 2003 %T EVA: evaluation of protein structure prediction servers %B Nucleic Acids Res %V 31 %N 13 %P 3311-3315 %! EVA: evaluation of protein structure prediction servers %@ 0305-1048 %R 10.1093/nar/gkg619 %M 12824315 %L 121 %F 121 %X EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods. %U http://salilab.org/pdf/Koh_NucleicAcidsRes_2003.pdf %0 Journal Article %A Kolb, P. %A Phan, K. %A Gao, Z. %A Marko, A. %A Sali, A. %A Jacobson, K.A. %D 2012 %T Limits of ligand selectivity from docking to models: In silico screening for A1 adenosine receptor antagonists %B PLoS One %V 7 %P e49910 %! Limits of ligand selectivity from docking to models: In silico screening for A1 adenosine receptor antagonists %2 PMCID3503826 %M 23185482;PMCID:PMC3503826 %L 285 %F 285 %U http://salilab.org/pdf/Kolb_PloSOne_2012.pdf %0 Journal Article %A Kollman, J. %A Greenberg, C. %A Li, S. %A Moritz, M. %A Zelter, A. %A Fong, K. %A Fernandez, J. %A Sali, A. %A Kilmartin, J. %A Davis, T. %A Agard, D. %D 2015 %T Ring closure activates yeast γTuRC for species-specific microtubule nucleation %B Nat Struct Mol Biol %V 22 %P 132-137 %! Ring closure activates yeast γTuRC for species-specific microtubule nucleation %2 PMCID4318760 %M 25599398;PMCID:PMC4318760 %L 334 %F 334 %U http://salilab.org/pdf/Kollman_NatStructMolBiol_2015.pdf %0 Journal Article %A Korkin, D. %A Davis, F. P. %A Alber, F. %A Luong, T. %A Shen, M. Y. %A Lucic, V. %A Kennedy, M. B. %A Sali, A. %D 2006 %T Structural modeling of protein interactions by analogy: application to PSD-95 %B PLoS Computational Biology %V 2 %N 11 %P e153 %8 Nov 10 %! Structural modeling of protein interactions by analogy: application to PSD-95 %M 17096593 %L 162 %F 162 %K Amino Acid Sequence Binding Sites Computer Simulation Dimerization Intracellular Signaling Peptides and Proteins/*chemistry Membrane Proteins/*chemistry/*ultrastructure *Models, Chemical *Models, Molecular Molecular Sequence Data Multiprotein Complexes/chemistry/ultrastructure Protein Binding Protein Conformation Protein Interaction Mapping/*methods Sequence Analysis, Protein/*methods %X We describe comparative patch analysis for modeling the structures of multidomain proteins and protein complexes, and apply it to the PSD-95 protein. Comparative patch analysis is a hybrid of comparative modeling based on a template complex and protein docking, with a greater applicability than comparative modeling and a higher accuracy than docking. It relies on structurally defined interactions of each of the complex components, or their homologs, with any other protein, irrespective of its fold. For each component, its known binding modes with other proteins of any fold are collected and expanded by the known binding modes of its homologs. These modes are then used to restrain conventional molecular docking, resulting in a set of binary domain complexes that are subsequently ranked by geometric complementarity and a statistical potential. The method is evaluated by predicting 20 binary complexes of known structure. It is able to correctly identify the binding mode in 70% of the benchmark complexes compared with 30% for protein docking. We applied comparative patch analysis to model the complex of the third PSD-95, DLG, and ZO-1 (PDZ) domain and the SH3-GK domains in the PSD-95 protein, whose structure is unknown. In the first predicted configuration of the domains, PDZ interacts with SH3, leaving both the GMP-binding site of guanylate kinase (GK) and the C-terminus binding cleft of PDZ accessible, while in the second configuration PDZ interacts with GK, burying both binding sites. We suggest that the two alternate configurations correspond to the different functional forms of PSD-95 and provide a possible structural description for the experimentally observed cooperative folding transitions in PSD-95 and its homologs. More generally, we expect that comparative patch analysis will provide useful spatial restraints for the structural characterization of an increasing number of binary and higher-order protein complexes. %Z 1553-7358 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. %U http://salilab.org/pdf/Korkin_PLoSComputationalBiology_2006.pdf %+ Department of Biopharmaceutical Sciences, University of California San Francisco, San Francisco, California, United States of America. %0 Journal Article %A Korkin, D %A Davis, FP %A Sali, A %D 2005 %T Localization of protein-binding sites within families of proteins %B Protein Sci %V 14 %N 9 %P 2350-2360 %8 Sep %! Localization of protein-binding sites within families of proteins. %@ 0961-8368 %M 16081657 %L 152 %F 152 %K Acyl-CoA Dehydrogenase Binding Sites Computational Biology Databases, Protein Evolution, Molecular Lectins, C-Type Models, Molecular Protein Conformation Protein Structure, Tertiary Proteins %X We address the question of whether or not the positions of protein-binding sites on homologous protein structures are conserved irrespective of the identities of their binding partners. First, for each domain family in the Structural Classification of Proteins (SCOP), protein-binding sites are extracted from our comprehensive database of structurally defined binary domain interactions (PIBASE). Second, the binding sites within each family are superposed using a structural alignment of its members. Finally, the degree of localization of binding sites within each family is quantified by comparing it with localization expected by chance. We found that 72% of the 1847 SCOP domain families in PIBASE have binding sites with localization values greater than expected by chance. Moreover, 554 (30%) of these families have localizations that are statistically significant (i.e., more than four standard deviations away from the mean expected by chance). In contrast, only 144 (8%) families have significantly low localization. The absence of a significant correlation of the binding site localization with the average sequence and structural conservations in a family suggests that localization can be helpful for describing the functional diversity of protein-protein interactions, complementing measures of sequence and structural conservation. Consideration of the binding site localization may also result in spatial restraints for the modeling of protein assembly structures. %U http://salilab.org/pdf/Korkin_ProteinSci_2005.pdf %+ Department of Biopharmaceutical Sciences, University of California at San Francisco, San Francisco, CA 94143-2552, USA. %G eng %0 Journal Article %A Kostic, M. %A Lima, C. %A Sali, A. %D 2013 %T Celebrating 20 Years of Structural Biology %B Structure %V 21 %P 1477-1478 %! Celebrating 20 Years of Structural Biology %M 24010704;PMCID:PMC Journal- In Process %L 310 %F 310 %U http://salilab.org/pdf/Lima_Structure_2013.pdf %0 Journal Article %A Kotnik, M. %A Sali, A. %A Kos, J. %A Turk, B. %A Turk, V. %D 1987 %T Nova metoda za hitro dolocanje kineticnih konstant pri interakciji encima s kompetitivnim inhibitorjem (A new method for rapid determination of kinetic constants for competitive inhibition of enzymes) %B Vestnik Slovenskaga Kemijskega Drustva %V 34 %P 369-377 %8 1987/// %! Nova metoda za hitro dolocanje kineticnih konstant pri interakciji encima s kompetitivnim inhibitorjem (A new method for rapid determination of kinetic constants for competitive inhibition of enzymes) %L 4 %F 4 %K Enzymes %Z TY - JOUR %U http://salilab.org/pdf/Kotnik_VestnikSlovenskagaKemijskegaDrustva_1987.pdf %0 Journal Article %A Koulich, D. %A Orlova, M. %A Malhotra, A. %A Sali, A. %A Darst, S. A. %A Borukhov, S. %D 1997 %T Domain organization of Escherichia coli transcript cleavage factors GreA and GreB %B J Biol Chem %V 272 %N 11 %P 7201-7210 %8 Mar 14 %! Domain organization of Escherichia coli transcript cleavage factors GreA and GreB %M 9054416 %L 56 %F 56 %K Amino Acid Sequence Bacterial Proteins/*chemistry/genetics Escherichia coli/*genetics *Escherichia coli Proteins Molecular Sequence Data Peptide Elongation Factors/*chemistry/genetics Peptide Mapping Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Alignment Sequence Analysis Transcription Factors/*chemistry/genetics Transcription, Genetic %X The GreA and GreB proteins of Escherichia coli induce cleavage of the nascent transcript in ternary elongation complexes of RNA polymerase. Gre factors are presumed to have two biologically important and evolutionarily conserved functions: the suppression of elongation arrest and the enhancement of transcription fidelity. A three-dimensional structure of GreB was generated by homology modeling on the basis of the known crystal structure of GreA. Both factors display similar overall architecture and surface charge distribution, with characteristic C-terminal globular and N-terminal coiled-coil domains. One major difference between the two factors is the "basic patch" on the surface of the coiled-coil domain, which is much larger in GreB than in GreA. In both proteins, a site near the basic patch cross-links to the 3' terminus of RNA in the ternary transcription complex. GreA/GreB hybrid molecules were constructed by genetic engineering in which the N-terminal domain of one protein was fused to the C-terminal domain of the other. In the hybrid molecules, both the coiled-coil and the globular domains contribute to specific binding of Gre factors to RNA polymerase, whereas the antiarrest activity and the GreA or GreB specificity of transcript cleavage is determined by the N-terminal domain. These results implicate the basic patch of the N-terminal coiled-coil domain as an important functional element responsible for the interactions with nascent transcript and determining the size of the RNA fragment to be excised during the course of the cleavage reaction. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Koulich_JBiolChem_1997.pdf %+ Department of Microbiology and Immunology, State University of New York, Health Science Center at Brooklyn, Brooklyn, New York 11203, USA. %0 Journal Article %A Kozai, T. %A Fernandez-Martinez, J. %A van Eeuwen, T. %A Gallardo, P. %A Kapinos, L.E. %A Mazur, A. %A Zhang, W. %A Tempkin, J. %A Delgado-Izquierdo, M. %A Raveh, B. %A Sali, A. %A Chait, B.T. %A Veenhoff, L.M. %A Rout, M.P. %A Lim, R.Y.H. %D 2023 %T Dynamic molecular mechanism of the nuclear pore complex permeability barrier %B in press %! Dynamic molecular mechanism of the nuclear pore complex permeability barrier %R 10.1101/2023.03.31.535055 %2 PMCID10103940 %M 37066338 %L 442 %F 442 %0 Journal Article %A Kroetz, D. %A Ahituv, N. %A Burchard, E. %A Guo, S. %A Sali, A. %A Giacomini, K. %D 2009 %T The Pharmacogenomics Center of the University of California, Sam Francisco: At the interface of genomics, biological mechanism and drug therapy %B Pharmacogenomics %V 10 %P 1569-1576 %! The Pharmacogenomics Center of the University of California, Sam Francisco: At the interface of genomics, biological mechanism and drug therapy %2 PMCID2923222 %M 19842929;PMCID:PMC2923222 %L 234 %F 234 %U http://salilab.org/pdf/Kroetz_Pharmacogenomics_2009.pdf %0 Journal Article %A Krukenberg, KA %A Forster, F %A Rice, LM %A Sali, A %A Agard, DA %D 2008 %T Multiple conformations of E. coli Hsp90 in solution: insights into the conformational dynamics of Hsp90 %B Structure %V 16 %N 5 %P 755-765 %8 May %! Multiple conformations of E. coli Hsp90 in solution: insights into the conformational dynamics of Hsp90. %@ 0969-2126 %2 PMCID2600884 %M 18462680;PMCID:PMC2600884 %L 204 %F 204 %K Adenylyl Imidodiphosphate Dimerization Escherichia coli Escherichia coli Proteins HSP90 Heat-Shock Proteins Models, Molecular Protein Conformation Protein Structure, Tertiary Scattering, Small Angle Solutions X-Ray Diffraction %X Hsp90, an essential eukaryotic chaperone, depends upon its intrinsic ATPase activity for function. Crystal structures of the bacterial Hsp90 homolog, HtpG, and the yeast Hsp90 reveal large domain rearrangements between the nucleotide-free and the nucleotide-bound forms. We used small-angle X-ray scattering and recently developed molecular modeling methods to characterize the solution structure of HtpG and demonstrate how it differs from known Hsp90 conformations. In addition to this HtpG conformation, we demonstrate that under physiologically relevant conditions, multiple conformations coexist in equilibrium. In solution, nucleotide-free HtpG adopts a more extended conformation than observed in the crystal, and upon the addition of AMPPNP, HtpG is in equilibrium between this open state and a closed state that is in good agreement with the yeast AMPPNP crystal structure. These studies provide a unique view of Hsp90 conformational dynamics and provide a model for the role of nucleotide in effecting conformational change. %U http://salilab.org/pdf/Krukenberg_Structure_2008.pdf %+ Graduate Program in Chemistry and Chemical Biology, Department of Biochemistry & Biophysics and the Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA 94158, USA. %G eng %0 Journal Article %A Kwon, Y. %A Kaake, R. %A Echeverria, I. %A Suarez, M. %A Shamsabadi, M. K. %A Stoneham, C. %A Ramirez, P. W. %A Kress, J. %A Singh, R. %A Sali, A. %A Krogan, N. %A Guatelli, J. %A Jia, X. %D 2020 %T Structural Basis of CD4 Downregulation by HIV-1 Nef %B Nat Struct Mol Biol %! Structural Basis of CD4 Downregulation by HIV-1 Nef %R 10.1101/2020.04.21.054007 %2 PMCID7483821 %M 32719457 %L 403 %F 403 %U https://salilab.org/pdf/Kwon_NatStructMolBiol_2020.pdf %0 Journal Article %A Lah, T. %A Kregar, I. %A Sali, A. %A Lenarcic, B. %A Kotnik, M. %A Kostka, V. %A Turk, V. %D 1988 %T Circular dichroism studies of different aspartyl proteinases and their interactions with pepstatin %B Periodicum Biologorum %V 90 %P 31-38 %8 1988/// %! Circular dichroism studies of different aspartyl proteinases and their interactions with pepstatin %L 6 %F 6 %K Circular Dichroism %Z TY - JOUR %U http://salilab.org/pdf/Lah_PeriodicumBiologorum_1988.pdf %0 Journal Article %A Lasater, E. %A Massi, E. %A Stecula, A. %A Politi, J. %A Tan, S. %A Smith, C. %A Gunthorpe, M. %A Holmes, J. %A Chehab, F. %A Sali, A. %A Shah, N. %D 2016 %T Novel TKI-resistant BCR-ABL1 Gatekeeper Residue Mutations Retain in vitro Sensitivity to Axitinib %B Leukemia %V 30 %N 6 %P 1405-9 %! Novel TKI-resistant BCR-ABL1 Gatekeeper Residue Mutations Retain in vitro Sensitivity to Axitinib %R 10.1038/leu.2015.303 %M 26511402 %L 347 %F 347 %U https://salilab.org/pdf/Lasater_Leukemia_2015.pdf %0 Journal Article %A Lasker, K. %A Forster, F. %A Bohn, S. %A Walzthoeni, T. %A Villa, E. %A Unverdorben, P. %A Beck, F. %A Aebersold, R. %A Sali, A. %A Baumeister, W. %D 2012 %T Molecular architecture of the 26S proteasome holocomplex determined by an integrative approach %B Proc Natl Acad Sci USA %V 109 %P 1380-1387 %! Molecular architecture of the 26S proteasome holocomplex determined by an integrative approach %2 PMCID3277140 %M 22307589;PMCID:PMC3277140 %L 274 %F 274 %U http://salilab.org/pdf/Lasker_ProcNatlAcadSciUSA_2012.pdf %0 Journal Article %A Lasker, K. %A Phillips, J.L. %A Russel, D. %A Velazquez-Muriel, J. %A Schneidman-Duhovny, D. %A Webb, B. %A Schlessinger, A. %A Sali, A. %D 2010 %T Integrative Structure Modeling of Macromolecular Assemblies from Proteomics Data %B Mol Cell Proteomics %V 9 %N 8 %P 1689-1702 %! Integrative Structure Modeling of Macromolecular Assemblies from Proteomics Data %2 PMCID2938050 %M 20507923;PMCID:PMC2938050 %L 241 %F 241 %U http://salilab.org/pdf/Lasker_MolCellProteomics_2010.pdf %0 Journal Article %A Lasker, K. %A Sali, A. %A Wolfson, H.J. %D 2010 %T Determining macromolecular assembly structures by molecular docking and fitting into an electron density map %B Proteins:Struct Funct Bioinform %V 78 %P 3205-3211 %! Determining macromolecular assembly structures by molecular docking and fitting into an electron density map %2 PMCID2952722 %M 20827723;PMCID:PMC2952722 %L 244 %F 244 %U http://salilab.org/pdf/Lasker_Proteins-StructFunctBioinform_2010a.pdf %0 Book Section %A Lasker, K. %A Velazquez-Muriel, J. %A Webb, B. %A Yang, Z. %A Ferrin, T.E. %A Sali, A. %D 2012 %T Macromolecular assembly structures by comparative modeling and electron microscopy %E Walker, J. %B Methods in Molecular Biology %C New York, NY %I Humana Press %P 331-350 %! Macromolecular assembly structures by comparative modeling and electron microscopy %M 22323229;PMCID:PMC Journal- In Process %L 267 %F 267 %U http://salilab.org/pdf/Lasker_MethodsMolBiol_2011.pdf %0 Journal Article %A Lasker, K %A Topf, M %A Sali, A %A Wolfson, HJ %D 2009 %T Inferential optimization for simultaneous fitting of multiple components into a cryoEM map of their assembly %B J Mol Biol %V 388 %N 1 %P 180-194 %8 24 April 2009 %! Inferential optimization for simultaneous fitting of multiple components into a cryoEM map of their assembly. %@ 1089-8638 %2 PMCID2680734 %M 19233204;PMCID:PMC2680734 %L 226 %F 226 %X Models of macromolecular assemblies are essential for a mechanistic description of cellular processes. Such models are increasingly obtained by fitting atomic-resolution structures of components into a density map of the whole assembly. Yet, current density-fitting techniques are frequently insufficient for an unambiguous determination of the positions and orientations of all components. Here, we describe MultiFit, a method for simultaneously fitting atomic structures of components into their assembly density map at resolutions as low as 25 A. The component positions and orientations are optimized with respect to a scoring function that includes the quality-of-fit of components in the map, the protrusion of components from the map envelope, as well as the shape complementarity between pairs of components. The scoring function is optimized by our exact inference optimizer DOMINO that efficiently finds the global minimum in a discrete sampling space. MultiFit was benchmarked on 7 assemblies of known structure, consisting of up to 7 proteins each. The input atomic structures of the components were obtained from the Protein Data Bank as well as by comparative modeling based on 16 - 99% sequence identity to a template structure. A near-native configuration was usually found as the top-scoring model. Therefore, MultiFit can provide initial configurations for further refinement of many multi-component assembly structures described by electron microscopy. %U http://salilab.org/pdf/Lasker_JMolBiol_2009.pdf %+ Blavatnik School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco. %G Eng %0 Journal Article %A Latham, A. P. %A Tempkin, J. O. B. %A Otsuka, S. %A Zhang, W. %A Ellenberg, J. %A Sali, A. %D 2024 %T Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex %B bioRxiv %7 20240808 %8 Aug 08 %! Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex %@ 2692-8205 %R 10.1101/2024.08.06.606842 %1 Conflicts of Interest The authors declare no competing interests. %2 PMCID11326192 %M 39149317 %L 459 %F 459 %X Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source %Z Latham, Andrew P Tempkin, Jeremy O B Otsuka, Shotaro Zhang, Wanlu Ellenberg, Jan Sali, Andrej 2024/8/16 %U https://www.ncbi.nlm.nih.gov/pubmed/39149317 %+ Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany. %G eng %0 Journal Article %A Lee, S. A. %A Shen, E. L. %A Fiser, A. %A Sali, A. %A Guo, S. %D 2003 %T The zebrafish forkhead transcription factor Foxi1 specifies epibranchial placode-derived sensory neurons %B Development %V 130 %N 12 %P 2669-2679 %! The zebrafish forkhead transcription factor Foxi1 specifies epibranchial placode-derived sensory neurons %@ 0950-1991 %R 10.1242/dev.00502 %M 12736211 %L 125 %F 125 %X Vertebrate epibranchial placodes give rise to visceral sensory neurons that transmit vital information such as heart rate, blood pressure and visceral distension. Despite the pivotal roles they play, the molecular program underlying their development is not well understood. Here we report that the zebrafish mutation no soul, in which epibranchial placodes are defective, disrupts the fork head-related, winged helix domain-containing protein Foxi1. Foxi1 is expressed in lateral placodal progenitor cells. In the absence of foxi1 activity, progenitor cells fail to express the basic helix-loop-helix gene neurogenin that is essential for the formation of neuronal precursors, and the paired homeodomain containing gene phox2a that is essential for neuronal differentiation and maintenance. Consequently, increased cell death is detected indicating that the placodal progenitor cells take on an apoptotic pathway. Furthermore, ectopic expression of foxi1 is sufficient to induce phox2a-positive and neurogenin-positive cells. Taken together, these findings suggest that Foxi1 is an important determination factor for epibranchial placodal progenitor cells to acquire both neuronal fate and subtype visceral sensory identity. %U http://salilab.org/pdf/Lee_Development_2003.pdf %0 Journal Article %A Leitner, A. %A Bonvin, A. M. J. J. %A Borchers, C. H. %A Chalkley, R. J. %A Chamot-Rooke, J. %A Combe, C. W. %A Cox, J. %A Dong, M. %A Fischer, L. %A Götze, M. %A Gozzo, F. C. %A Heck, A. J. R. %A Hoopmann, M. R. %A Huang, L. %A Ishihama, Y. %A Jones, A. R. %A Kalisman, N. %A Kohlbacher, O. %A Mechtler, K. %A Moritz, R. L. %A Netz, E. %A Novak, P. %A Petrotchenko, E. %A Sali, A. %A Scheltema, R. A. %A Schmidt, C. %A Schriemer, D. %A Sinz, A. %A Sobott, F. %A Stengel, F. %A Thalassinos, K. %A Urlaub, H. %A Viner, R. %A Vizcaíno, J. A. %A Wilkins, M. R. %A Rappsilber, J. %D 2020 %T Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry %B Structure %V 28 %N 11 %P 1259-1268 %! Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry %R 10.1016/j.str.2020.09.011 %M 33065067 %L 407 %F 407 %U https://salilab.org/pdf/Leitner_Structure_2020.pdf %0 Book Section %A Lenarcic, B. %A Ritonja, A. %A Sali, A. %A Kotnik, M. %A Turk, V. %A Machleidt, W. %D 1986 %T Properties and structure of human spleen stefin B - a low molecular weight protein inhibitor of cysteine proteinases %E Turk, V. %B Cysteine Proteinases and Their Inhibitors; First International Symposium, Portoroz, Yugoslavia, September 15-18, 1985. Xvi+846p. %C Berlin, West Germany; New York, New York, USA. %I Walter De Gruyter and Co. %P 473-488 %! Properties and structure of human spleen stefin B - a low molecular weight protein inhibitor of cysteine proteinases %L 2 %F 2 %U http://salilab.org/pdf/Lenarcic_CystProt_1986.pdf %0 Journal Article %A Lensink, M. F. %A Velankar, S. %A Kryshtafovych, A. %A Huang, S. %A Schneidman-Duhovny, D. %A Sali, A. %A Segura, J. %A Fernandez-Fuentes, N. %A Viswanath, S. %A Elber, R. %A Grudinin, S. %A Popov, P. %A Neveu, E. %A Lee, H. %A Baek, M. %A Park, S. %A Heo, L. %A Lee, G. R. %A Seok, C. %A Qin, S. %A Zhou, H. %A Ritchie, D. W. %A Maigret, B. %A Devignes, M. %A Ghoorah, A. %A Torchala, M. %A Chaleil, R. A. G. %A Bates, P. A. %A Ben-Zeev, E. %A Eisenstein, M. %A Negi, S. S. %A Weng, Z. %A Vreven, T. %A Pierce, B. G. %A Borrman, T. M. %A Yu, J. %A Ochsenbein, F. %A Guerois, R. %A Vangone, A. %A Rodrigues, J. P. G. L. M. %A van Zundert, G. %A Nellen, M. %A Xue, L. %A Karaca, E. %A Melquiond, A. S. J. %A Visscher, K. %A Kastritis, P. L. %A Bonvin, A. M. J. J. %A Xu, X. %A Qiu, L. %A Yan, C. %A Li, J. %A Ma, Z. %A Cheng, J. %A Zou, X. %A Shen, Y. %A Peterson, L. X. %A Kim, H. %A Roy, A. %A Han, X. %A Esquivel-Rodriguez, J. %A Kihara, D. %A Yu, X. %A Bruce, N. J. %A Fuller, J. C. %A Wade, R. C. %A Anishchenko, I. %A Kundrotas, P. J. %A Vakser, I. A. %A Imai, K. %A Yamada, K. %A Oda, T. %A Nakamura, T. %A Tomii, K. %A Pallara, C. %A Romero-Durana, M. %A Jiménez-García, B. %A Moal, I. H. %A Férnandez-Recio, J. %A Joung, J. Y. %A Kim, J. Y. %A Joo, K. %A Lee, J. %A Kozakov, D. %A Vajda, S. %A Mottarella, S. %A Hall, D. R. %A Beglov, D. %A Mamonov, A. %A Xia, B. %A Bohnuud, T. %A Del Carpio, C. A. %A Ichiishi, E. %A Marze, N. %A Kuroda, D. %A Burman, S. S. R. %A Gray, J. J. %A Chermak, E. %A Cavallo, L. %A Oliva, R. %A Tovchigrechko, A. %A Wodak, S. J. %D 2016 %T Prediction of homo- and hetero-protein complexes by ab-initio and template-based docking: a CASP-CAPRI experiment %B Proteins %V 84 Suppl 1 %P 323-48 %! Prediction of homo- and hetero-protein complexes by ab-initio and template-based docking: a CASP-CAPRI experiment %R 10.1002/prot.25007 %2 PMCID5030136 %M 27122118 %L 350 %F 350 %U https://salilab.org/pdf/Lensink_Proteins_2016.pdf %0 Journal Article %A Lezon, T %A Sali, A %A Bahar, I %D 2009 %T Global motions of the nuclear pore complex:Insights from Elastic Network Models %B PLoS Comp Biol %V 5 %N 9 %P e1000496 %! Global motions of the nuclear pore complex:Insights from Elastic Network Models %2 PMCID2725293 %M 19730674;PMCID:PMC2725293 %L 230 %F 230 %U http://salilab.org/pdf/Lezon_PLoSComputationalBiology_2009.pdf %0 Journal Article %A Li, A. %A Zhang, S. %A Loconte, V. %A Liu, Y. %A Ekman, A. %A Thompson, G. J. %A Sali, A. %A Stevens, R. C. %A White, K. %A Singla, J. %A Sun, L. %D 2022 %T An Intensity-based Post-processing Tool for 3D Instance Segmentation of Organelles in Soft X-ray Tomograms %B PLoS One %V 17 %6 9 %P e0269887 %! An Intensity-based Post-processing Tool for 3D Instance Segmentation of Organelles in Soft X-ray Tomograms %R 10.1371/journal.pone.0269887 %2 PMCID9436087 %M 36048824 %L 425 %F 425 %U https://salilab.org/pdf/Li_PLoSOne_2022a.pdf %0 Journal Article %A Li, A. %A Zhang, X. %A Singla, J. %A White, K. %A Loconte, V. %A Hu, C. %A Zhang, C. %A Li, S. %A Li, W. %A Francis, J. P. %A Wang, C. %A Sali, A. %A Sun, L. %A He, X. %A Stevens, R. C. %D 2022 %T Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion %B PLoS One %V 17 %N 3 %P e0265567 %7 20220324 %! Auto-segmentation and time-dependent systematic analysis of mesoscale cellular structure in β-cells during insulin secretion %@ 1932-6203 %R 10.1371/journal.pone.0265567 %1 The authors have declared that no competing interests exist. %2 PMCID8947144 %M 35324950 %L 434 %F 434 %K Glucose Insulin Insulin Secretion Insulin-Secreting Cells Mitochondria %X The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreatic β-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments. %Z Li, Angdi Zhang, Xiangyi Singla, Jitin White, Kate Loconte, Valentina Hu, Chuanyang Zhang, Chuyu Li, Shuailin Li, Weimin Francis, John Paul Wang, Chenxi Sali, Andrej Sun, Liping He, Xuming Stevens, Raymond C 2022/3/25 %U https://salilab.org/pdf/Li_PLoSOne_2022.pdf %+ iHuman Institute, ShanghaiTech University, Shanghai, China. School of Life Science and Technology, ShanghaiTech University, Shanghai, China. University of Chinese Academy of Sciences, Beijing, China. School of Information Science and Technology, ShanghaiTech University, Shanghai, China. Department of Biological Sciences, Bridge Institute, University of Southern California, Los Angeles, CA, United States of America. Department of Computer Science, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, United States of America. California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America. Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China. %G eng %0 Journal Article %A Li, M %A Hays, FA %A Roe-Zurz, Z %A Vuong, L %A Kelly, L %A Ho, CM %A Robbins, RM %A Pieper, U %A O'Connell, JD 3rd %A Miercke, LJ %A Giacomini, KM %A Sali, A %A Stroud, RM %D 2009 %T Selecting optimum eukaryotic integral membrane proteins for structure determination by rapid expression and solubilization screening %B J Mol Biol %V 385 %N 3 %P 820-830 %8 Jan %! Selecting optimum eukaryotic integral membrane proteins for structure determination by rapid expression and solubilization screening. %@ 1089-8638 %2 PMCID2659619 %M 19061901;PMCID:PMC2659619 %L 216 %F 216 %K Chromatography, Affinity Chromatography, Gel Humans Membrane Proteins Plasmids Protein Sorting Signals Saccharomyces cerevisiae Proteins Solubility %X A medium-throughput approach is used to rapidly identify membrane proteins from a eukaryotic organism that are most amenable to expression in amounts and quality adequate to support structure determination. The goal was to expand knowledge of new membrane protein structures based on proteome-wide coverage. In the first phase, membrane proteins from the budding yeast Saccharomyces cerevisiae were selected for homologous expression in S. cerevisiae, a system that can be adapted to expression of membrane proteins from other eukaryotes. We performed medium-scale expression and solubilization tests on 351 rationally selected membrane proteins from S. cerevisiae. These targets are inclusive of all annotated and unannotated membrane protein families within the organism's membrane proteome. Two hundred seventy-two targets were expressed, and of these, 234 solubilized in the detergent n-dodecyl-beta-D-maltopyranoside. Furthermore, we report the identity of a subset of targets that were purified to homogeneity to facilitate structure determinations. The extensibility of this approach is demonstrated with the expression of 10 human integral membrane proteins from the solute carrier superfamily. This discovery-oriented pipeline provides an efficient way to select proteins from particular membrane protein classes, families, or organisms that may be more suited to structure analysis than others. %Z PMC2659619 %U http://salilab.org/pdf/Li_JMolBiol_2009.pdf %+ Membrane Protein Expression Center, University of California at San Francisco, San Francisco, CA 94158-2517, USA. %G eng %0 Journal Article %A Li, W. %A Li, A. %A Yu, B. %A Zhang, X. %A Liu, X. %A White, K. L. %A Stevens, R. C. %A Baumeister, W. %A Sali, A. %A Jasnin, M. %A Sun, L. %D 2024 %T In situ structure of actin remodeling during glucose-stimulated insulin secretion using cryo-electron tomography %B Nat Commun %V 15 %N 1 %P 1311 %7 20240212 %8 Feb 12 %! In situ structure of actin remodeling during glucose-stimulated insulin secretion using cryo-electron tomography %@ 2041-1723 %R 10.1038/s41467-024-45648-7 %1 The authors declare no competing interests. %2 PMCID10861521 %M 38346988 %L 452 %F 452 %K Insulin Secretion Actins Glucose Electron Microscope Tomography Insulin Insulin-Secreting Cells Actin Cytoskeleton %X Actin mediates insulin secretion in pancreatic β-cells through remodeling. Hampered by limited resolution, previous studies have offered an ambiguous depiction as depolymerization and repolymerization. We report the in situ structure of actin remodeling in INS-1E β-cells during glucose-stimulated insulin secretion at nanoscale resolution. After remodeling, the actin filament network at the cell periphery exhibits three marked differences: 12% of actin filaments reorient quasi-orthogonally to the ventral membrane; the filament network mainly remains as cell-stabilizing bundles but partially reconfigures into a less compact arrangement; actin filaments anchored to the ventral membrane reorganize from a "netlike" to a "blooming" architecture. Furthermore, the density of actin filaments and microtubules around insulin secretory granules decreases, while actin filaments and microtubules become more densely packed. The actin filament network after remodeling potentially precedes the transport and release of insulin secretory granules. These findings advance our understanding of actin remodeling and its role in glucose-stimulated insulin secretion. %Z Li, Weimin Li, Angdi Yu, Bing Zhang, Xiaoxiao Liu, Xiaoyan White, Kate L Stevens, Raymond C Baumeister, Wolfgang Sali, Andrej Jasnin, Marion Sun, Liping 2024/2/13 %U https://salilab.org/pdf/Li_Nature_2024.pdf %+ iHuman Institute, ShanghaiTech University, Shanghai, 201210, China. School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China. Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, 90089, USA. iHuman Institute, ShanghaiTech University, Shanghai, 201210, China. baumeist@biochem.mpg.de. Department of Molecular Structural Biology, Max Planck Institute of Biochemistry, 82152, Martinsried, Germany. baumeist@biochem.mpg.de. Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, 94158, USA. sali@salilab.org. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA. sali@salilab.org. Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA. sali@salilab.org. Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764, Neuherberg, Germany. marion.jasnin@helmholtz-munich.de. Department of Chemistry, Technical University of Munich, 85748, Garching, Germany. marion.jasnin@helmholtz-munich.de. iHuman Institute, ShanghaiTech University, Shanghai, 201210, China. sunlp@shanghaitech.edu.cn. %G eng %0 Journal Article %A Liedtke, W. %A Choe, Y. %A Marti-Renom, M. A. %A Bell, A. M. %A Denis, C. S. %A Sali, A. %A Hudspeth, A. J. %A Friedman, J. M. %A Heller, S. %D 2000 %T Vanilloid receptor-related osmotically activated channel (VR-OAC), a candidate vertebrate osmoreceptor %B Cell %V 103 %N 3 %P 525-535 %8 Oct 27 %! Vanilloid receptor-related osmotically activated channel (VR-OAC), a candidate vertebrate osmoreceptor %M 11081638 %L 86 %F 86 %K Amino Acid Sequence Animals Ankyrin Repeat/genetics/physiology Brain/cytology/metabolism/physiology CHO Cells Calcium Signaling Cations/metabolism Chickens/genetics Chromosomes, Human, Pair 12/genetics Chromosomes, Human, Pair 17/genetics Cloning, Molecular Electrophysiology Gene Expression Profiling Hair Cells, Inner/chemistry/metabolism/physiology Hamsters Humans Hypotonic Solutions In Situ Hybridization *Ion Channel Gating Merkel Cells/chemistry/metabolism Mice Molecular Sequence Data Neurons, Afferent/chemistry/metabolism/physiology Osmolar Concentration *Osmotic Pressure Phylogeny RNA, Messenger/analysis/genetics Radiation Hybrid Mapping Rats Receptors, Drug/chemistry/genetics/*metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Alignment %X The detection of osmotic stimuli is essential for all organisms, yet few osmoreceptive proteins are known, none of them in vertebrates. By employing a candidate-gene approach based on genes encoding members of the TRP superfamily of ion channels, we cloned cDNAs encoding the vanilloid receptor-related osmotically activated channel (VR-OAC) from the rat, mouse, human, and chicken. This novel cation-selective channel is gated by exposure to hypotonicity within the physiological range. In the central nervous system, the channel is expressed in neurons of the circumventricular organs, neurosensory cells responsive to systemic osmotic pressure. The channel also occurs in other neurosensory cells, including inner-ear hair cells, sensory neurons, and Merkel cells. %Z 0092-8674 Journal Article %U http://salilab.org/pdf/Liedtke_Cell_2000.pdf %+ Laboratory of Molecular Genetics, The Rockefeller University, New York, New York 10021, USA. %0 Journal Article %A Lima, C. %A Puglisi, J. %A Sali, A. %A Szewczak, L. %D 2006 %T Editorial %B Structure %V 14 %P 801 %! Editorial %L 159 %F 159 %U http://salilab.org/pdf/Lima_Structure_2006.pdf %0 Journal Article %A Lin, Z. %A Schaefer, K. %A Lui, I. %A Yao, Z. %A Fossati, A.A. %A Swaney, D.L. %A Palar, A. %A Sali, A. %A Wells, J.A. %D 2023 %T Multi-scale photocatalytic proximity labeling reveals cell surface neighbors on and between cells %B in press %! Multi-scale photocatalytic proximity labeling reveals cell surface neighbors on and between cells %R 10.1101/2023.10.28.564055 %L 447 %F 447 %0 Journal Article %A Loconte, V. %A Singla, J. %A Li, A. %A Chen, J. H. %A Ekman, A. %A McDermott, G. %A Sali, A. %A Le Gros, M. %A White, K. L. %A Larabell, C. A. %D 2022 %T Soft X-ray tomography to map and quantify organelle interactions at the mesoscale %B Structure %V 30 %N 4 %P 510-521 %7 20220202 %8 Feb 02 %! Soft X-ray tomography to map and quantify organelle interactions at the mesoscale %@ 1878-4186 %R 10.1016/j.str.2022.01.006 %1 Declaration of interests The authors declare no competing interests. %2 PMCID9013509 %M 35148829 %L 430 %F 430 %K 3D cell mapping mesoscale analysis organelle interaction pancreatic β cell soft X-ray tomography %X Inter-organelle interactions are a vital part of normal cellular function; however, these have proven difficult to quantify due to the range of scales encountered in cell biology and the throughput limitations of traditional imaging approaches. Here, we demonstrate that soft X-ray tomography (SXT) can be used to rapidly map ultrastructural reorganization and inter-organelle interactions in intact cells. SXT takes advantage of the naturally occurring, differential X-ray absorption of the carbon-rich compounds in each organelle. Specifically, we use SXT to map the spatiotemporal evolution of insulin vesicles and their co-localization and interaction with mitochondria in pancreatic β cells during insulin secretion and in response to different stimuli. We quantify changes in the morphology, biochemical composition, and relative position of mitochondria and insulin vesicles. These findings highlight the importance of a comprehensive and unbiased mapping at the mesoscale to characterize cell reorganization that would be difficult to detect with other existing methodologies. %Z Loconte, Valentina Singla, Jitin Li, Angdi Chen, Jian-Hua Ekman, Axel McDermott, Gerry Sali, Andrej Le Gros, Mark White, Kate L Larabell, Carolyn A 2022/2/13 %U https://salilab.org/pdf/Loconte_Structure_2022.pdf %+ iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China. Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. Department of Bioengineering and Therapeutic Science, Department of Pharmaceutical Chemistry, California Institute of Quantitative Bioscience, University of California San Francisco, San Francisco, CA 94158, USA. Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: katewhit@usc.edu. Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. Electronic address: carolyn.larabell@ucsf.edu. %G eng %0 Journal Article %A LoPiccolo, J. %A Kim, S. J. %A Shi, Y. %A Wu, B. %A Wu, H. %A Chait, B. T. %A Singer, R. H. %A Sali, A. %A Brenowitz, M. %A Bresnick, A. R. %A Backer, J. M. %D 2015 %T Assembly and Molecular Architecture of the Phosphoinositide 3-Kinase p85α Homodimer %B J Biol Chem %V 290 %N 51 %P 30390-405 %! Assembly and Molecular Architecture of the Phosphoinositide 3-Kinase p85α Homodimer %R 10.1074/jbc.M115.689604 %2 PMCID4683262 %M 26475863 %L 345 %F 345 %U https://salilab.org/pdf/LoPiccolo_JBiolChem_2015.pdf %0 Journal Article %A Luo, J. %A Cimermancic, P. %A Viswanath, S. %A Ebmeier, C. %A Kim, B. %A Dehecq, M. %A Raman, V. %A Greenberg, C. %A Pellarin, R. %A Sali, A. %A Taatjes, D. %A Hahn, S. %A Ranish, J. %D 2015 %T Architecture of the human and yeast general transcription and DNA repair factor TFIIH %B Mol Cell %V 59 %N 5 %P 794-806 %! Architecture of the human and yeast general transcription and DNA repair factor TFIIH %2 PMCID4560838 %M 26340423 %L 339 %F 339 %U http://salilab.org/pdf/Luo_MolCell_2015.pdf %0 Journal Article %A Lyon, A %A Zelter, A %A Viswanath, S %A Maxwell, A %A Johnson, R %A Yabut, KCB %A MacCoss, M %A Davis, TN %A Muller, E %A Sali, A %A Agard, DA %T Spc110 N-Terminal Domains Act Independently to Mediate Stable γ-Tubulin Small Complex Binding and γ-Tubulin Ring Complex Assembly %B submitted %! Spc110 N-Terminal Domains Act Independently to Mediate Stable γ-Tubulin Small Complex Binding and γ-Tubulin Ring Complex Assembly %R 10.1101/311027 %L 387 %F 387 %0 Journal Article %A Madhusudhan, M. S. %A Marti-Renom, M. A. %A Sanchez, R. %A Sali, A. %D 2006 %T Variable gap penalty for protein sequence-structure alignment %B Protein Engineering, Design & Selection %V 19 %N 3 %P 129-133 %8 Mar %! Variable gap penalty for protein sequence-structure alignment %M 16423846 %L 158 %F 158 %K Algorithms Amino Acid Sequence Models, Molecular Molecular Sequence Data Proteins/*chemistry Sequence Alignment/*methods Sequence Analysis, Protein/*methods *Sequence Homology, Amino Acid *Software %X The penalty for inserting gaps into an alignment between two protein sequences is a major determinant of the alignment accuracy. Here, we present an algorithm for finding a globally optimal alignment by dynamic programming that can use a variable gap penalty (VGP) function of any form. We also describe a specific function that depends on the structural context of an insertion or deletion. It penalizes gaps that are introduced within regions of regular secondary structure, buried regions, straight segments and also between two spatially distant residues. The parameters of the penalty function were optimized on a set of 240 sequence pairs of known structure, spanning the sequence identity range of 20-40%. We then tested the algorithm on another set of 238 sequence pairs of known structures. The use of the VGP function increases the number of correctly aligned residues from 81.0 to 84.5% in comparison with the optimized affine gap penalty function; this difference is statistically significant according to Student's t-test. We estimate that the new algorithm allows us to produce comparative models with an additional approximately 7 million accurately modeled residues in the approximately 1.1 million proteins that are detectably related to a known structure. %Z 1741-0126 (Print) Comparative Study Journal Article Research Support, N.I.H., Extramural %U http://salilab.org/pdf/Madhusudhan_ProteinEngineering_2006.pdf %+ Department of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California at San Francisco, 94143, USA. %0 Book Section %A Madhusudhan, M.S. %A Marti-Renom, M.A. %A Eswar, N. %A John, B. %A Pieper, U. %A Karchin, R. %A Shen, Min-yi %A Sali, A. %D 2005 %T Comparative Protein Structure Modeling %B Proteomics Protocols Handbook. Ed: J.M. Walker %C Totowa, NJ %I Humana Press Inc. %P 831-860 %! Comparative Protein Structure Modeling %L 145 %F 145 %U http://salilab.org/pdf/Madhusudhan_ProteomicsProtocols_2005.pdf %0 Journal Article %A Madhusudhan, M.S. %A Webb, B.M. %A Marti-Renom, M. A. %A Eswar, N. %A Sali, A. %D 2009 %T Alignment of multiple protein structures based on sequence and structure features %B Protein Eng Des Sel %V 22 %P 569-574 %! Alignment of multiple protein structures based on sequence and structure features %2 PMCID2909824 %M 19587024;PMCID:PMC2909824 %L 227 %F 227 %U http://salilab.org/pdf/Madhusudhan_ProteinEngineering_2009.pdf %0 Journal Article %A Mahrus, S. %A Trinidad, J. C. %A Barkan, D. T. %A Sali, A. %A Burlingame, A. L. %A Wells, J. A. %D 2008 %T Global Sequencing of Proteolytic Cleavage Sites in Apoptosis by Specific Labeling of Protein N Termini %B Cell %V 134 %P 866-876 %8 Aug 20 %! Global Sequencing of Proteolytic Cleavage Sites in Apoptosis by Specific Labeling of Protein N Termini %O Cell %@ 1097-4172 (Electronic) %2 PMCID2566540 %M 18722006;PMCID:PMC2566540 %L 209 %F 209 %X The nearly 600 proteases in the human genome regulate a diversity of biological processes, including programmed cell death. Comprehensive characterization of protease signaling in complex biological samples is limited by available proteomic methods. We have developed a general approach for global identification of proteolytic cleavage sites using an engineered enzyme to selectively biotinylate free protein N termini for positive enrichment of corresponding N-terminal peptides. Using this method to study apoptosis, we have sequenced 333 caspase-like cleavage sites distributed among 292 protein substrates. These sites are generally not predicted by in vitro caspase substrate specificity but can be used to predict other physiological caspase cleavage sites. Structural bioinformatic studies show that caspase cleavage sites often appear in surface-accessible loops and even occasionally in helical regions. Strikingly, we also find that a disproportionate number of caspase substrates physically interact, suggesting that these dimeric proteases target protein complexes and networks to elicit apoptosis. %Z PMC2566540 %U http://salilab.org/pdf/Mahrus_Cell_2008.pdf %+ Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA 94158, USA. %G Eng %0 Journal Article %A Manna, PT %A Obado, S %A Boehm, C %A Gadelha, C %A van Miero, G %A Sali, A %A Chait, B %A Rout, M %A Field, MC %D 2017 %T Lineage-specific proteins essential for endocytosis in trypanosomes %B J Cell Sci %V 130 %N 8 %P 1379-1392 %! Lineage-specific proteins essential for endocytosis in trypanosomes %R 10.1242/jcs.191478 %2 PMCID5399782 %M 28232524 %L 371 %F 371 %U https://salilab.org/pdf/Manna_JCellSci_2017.pdf %0 Journal Article %A Markley, J.L. %A Akutsu, H. %A Asakura, T. %A Baldus, M. %A Boelens, R. %A Bonvin, A. %A Kaptein, R. %A Bax, A. %A Bezsonova, I. %A Gryk, M.R. %A Hoch, J.C. %A Korzhnev, D.M. %A Maciejewski, M. %A Case, D. %A Chazin, W. %A Cross, T. %A Dames, S. %A Kessler, H. %A Lange, O. %A Madl, T. %A Reif, B. %A Sattler, M. %A Eliezer, D. %A Fersht, A. %A Forman-Kay, J. %A Kay, L. %A Fraser, J. %A Gross, J. %A Kortemme, T. %A Sali, A. %A Fujiwara, T. %A Gardner, K. %A Luo, X. %A Rizo-Rey, J. %A Rosen, M. %A Gil, R. %A HO, C. %A Rule, G. %A Gronenborn, A. %A Ishima, R. %A Klein-Seetharaman, J. %A Tang, P %A van der Wel, P. %A Xu, Y. %A Grzesiek, S. %A Hiller, S. %A Seelig, J. %A Laue, E. %A Mott, H. %A Nietlispach, D. %A Barsukiv, I. %A Lian, L. %A Middleton, D. %A Blumenschein, T. %A Moore, G. %A Campbell, I. %A Schnell, J. %A Vakonakis, I. %A Watts, A. %A Conte, M. %A Mason, J. %A Pfuhl, M. %A Sanderson, M. %A Craven, J. %A Williamson, M. %A Dominguez, C. %A Roberts, G. %A Gunther, U. %A Overduin, M. %A Werner, J. %A Williamson, P. %A Blindauer, C. %A Crump, M. %A Driscoll, P. %A Frenkiel, T. %A Golovanov, A. %A Matthews, S. %A Parkinson, J. %A Uhrin, D. %A Williams, M. %A Neuhaus, D. %A Oschkinat, H. %A Ramos, A. %A Shaw, D. %A Steinbeck, C. %A Vendruscolo, M. %A Vuister, G. %A Walters, K. %A Weinstein, H. %A Wuthrich, K. %A Yodoyama, S. %D 2012 %T In support of the BMRB %B Nat Struct Mol Biol %V 19 %P 854-860 %! In support of the BMRB %2 PMCID TBD by Journal %M 22955930;PMCID:PMC Journal- In Process %L 288 %F 288 %U http://salilab.org/pdf/Markley_NatStructMolBiol_2012.pdf %0 Journal Article %A Marti-Renom, M. A. %A Ilyin, V. A. %A Sali, A. %D 2001 %T DBAli: a database of protein structure alignments %B Bioinformatics %V 17 %N 8 %P 746-747 %! DBAli: a database of protein structure alignments %@ 1367-4803 %M 11524379 %L 93 %F 93 %X The DBAli database includes approximately 35 000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. %U http://salilab.org/pdf/Marti-Renom_Bioinformatics_2001.pdf %0 Journal Article %A Marti-Renom, M. A. %A Madhusudhan, M. S. %A Fiser, A. %A Rost, B. %A Sali, A. %D 2002 %T Reliability of assessment of protein structure prediction methods %B Structure %V 10 %N 3 %P 435-440 %! Reliability of assessment of protein structure prediction methods %@ 0969-2126 %M 12005441 %L 107 %F 107 %X The reliability of ranking of protein structure modeling methods is assessed. The assessment is based on the parametric Student's t test and the nonparametric Wilcox signed rank test of statistical significance of the difference between paired samples. The approach is applied to the ranking of the comparative modeling methods tested at the fourth meeting on Critical Assessment of Techniques for Protein Structure Prediction (CASP). It is shown that the 14 CASP4 test sequences may not be sufficient to reliably distinguish between the top eight methods, given the model quality differences and their standard deviations. We suggest that CASP needs to be supplemented by an assessment of protein structure prediction methods that is automated, continuous in time, based on several criteria applied to a large number of models, and with quantitative statistical reliability assigned to each characterization. %U http://salilab.org/pdf/Marti-Renom_Structure_2002.pdf %0 Journal Article %A Marti-Renom, M. A. %A Madhusudhan, M. S. %A Sali, A. %D 2004 %T Alignment of protein sequences by their profiles %B Protein Sci %V 13 %N 4 %P 1071-1087 %8 Apr %! Alignment of protein sequences by their profiles %M 15044736 %L 131 %F 131 %K *Algorithms Amino Acid Sequence Computational Biology Databases, Protein Markov Chains Molecular Sequence Data *Protein Folding Protein Structure, Tertiary Proteins/*chemistry Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. *Sequence Alignment Sequence Homology *Software %X The accuracy of an alignment between two protein sequences can be improved by including other detectably related sequences in the comparison. We optimize and benchmark such an approach that relies on aligning two multiple sequence alignments, each one including one of the two protein sequences. Thirteen different protocols for creating and comparing profiles corresponding to the multiple sequence alignments are implemented in the SALIGN command of MODELLER. A test set of 200 pairwise, structure-based alignments with sequence identities below 40% is used to benchmark the 13 protocols as well as a number of previously described sequence alignment methods, including heuristic pairwise sequence alignment by BLAST, pairwise sequence alignment by global dynamic programming with an affine gap penalty function by the ALIGN command of MODELLER, sequence-profile alignment by PSI-BLAST, Hidden Markov Model methods implemented in SAM and LOBSTER, pairwise sequence alignment relying on predicted local structure by SEA, and multiple sequence alignment by CLUSTALW and COMPASS. The alignment accuracies of the best new protocols were significantly better than those of the other tested methods. For example, the fraction of the correctly aligned residues relative to the structure-based alignment by the best protocol is 56%, which can be compared with the accuracies of 26%, 42%, 43%, 48%, 50%, 49%, 43%, and 43% for the other methods, respectively. The new method is currently applied to large-scale comparative protein structure modeling of all known sequences. %Z 0961-8368 Journal Article %U http://salilab.org/pdf/Marti-Renom_ProteinSci_2004.pdf %+ Mission Bay Genentech Hall, University of California, San Francisco, San Francisco, CA 94143, USA. marcius@salilab.org %0 Journal Article %A Marti-Renom, M. A. %A Pieper, U. %A Madhusudhan, M. S. %A Rossi, A. %A Eswar, N. %A Davis, F. P. %A Al-Shahrour, F. %A Dopazo, J. %A Sali, A. %D 2007 %T DBAli tools: mining the protein structure space %B Nucleic Acids Res %V 35 %N Web Server issue %P W393-397 %8 Jul 1 %! DBAli tools: mining the protein structure space %M 17478513 %L 183 %F 183 %K *Algorithms Amino Acid Sequence Computational Biology/*methods Data Interpretation, Statistical *Databases, Protein Internet Molecular Sequence Data Protein Conformation Proteins/*chemistry/classification/*metabolism Pseudomonas aeruginosa/*metabolism Sequence Alignment/*methods Sequence Analysis, Protein/*methods Sequence Homology, Amino Acid *Software Structure-Activity Relationship %X The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions. %Z 1362-4962 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Marti-Renom_NucleicAcidsRes_2007.pdf %+ Structural Genomics Unit, and California Institute for Quantitative Biomedical Research, University of California at San Francisco, San Francisco, CA 94158-2330, USA. mmarti@cipf.es %0 Journal Article %A Marti-Renom, M. A. %A Rossi, A. %A Al-Shahrour, F. %A Davis, F. P. %A Pieper, U. %A Dopazo, J. %A Sali, A. %D 2007 %T The AnnoLite and AnnoLyze programs for comparative annotation of protein structures %B BMC Bioinformatics %V 8 Suppl 4 %P S4 %! The AnnoLite and AnnoLyze programs for comparative annotation of protein structures %M 17570147 %L 182 %F 182 %K *Algorithms Amino Acid Sequence Confidence Intervals Data Interpretation, Statistical *Databases, Protein Information Storage and Retrieval/methods Molecular Sequence Data Proteins/*chemistry/classification/*metabolism Sensitivity and Specificity Sequence Alignment/*methods Sequence Analysis, Protein/*methods Sequence Homology, Amino Acid *Software Structure-Activity Relationship %X BACKGROUND: Advances in structural biology, including structural genomics, have resulted in a rapid increase in the number of experimentally determined protein structures. However, about half of the structures deposited by the structural genomics consortia have little or no information about their biological function. Therefore, there is a need for tools for automatically and comprehensively annotating the function of protein structures. We aim to provide such tools by applying comparative protein structure annotation that relies on detectable relationships between protein structures to transfer functional annotations. Here we introduce two programs, AnnoLite and AnnoLyze, which use the structural alignments deposited in the DBAli database. DESCRIPTION: AnnoLite predicts the SCOP, CATH, EC, InterPro, PfamA, and GO terms with an average sensitivity of ~90% and average precision of ~80%. AnnoLyze predicts ligand binding site and domain interaction patches with an average sensitivity of ~70% and average precision of ~30%, correctly localizing binding sites for small molecules in ~95% of its predictions. CONCLUSION: The AnnoLite and AnnoLyze programs for comparative annotation of protein structures can reliably and automatically annotate new protein structures. The programs are fully accessible via the Internet as part of the DBAli suite of tools at http://salilab.org/DBAli/. %Z 1471-2105 (Electronic) Journal Article Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Marti-Renom_BMCBioinformatics_2007.pdf %+ Structural Genomics Unit, Bioinformatics Department, Centro de Investigacion Principe Felipe, Valencia, Spain. mmarti@cipf.es %0 Journal Article %A Marti-Renom, M. A. %A Stuart, A. C. %A Fiser, A. %A Sanchez, R. %A Melo, F. %A Sali, A. %D 2000 %T Comparative protein structure modeling of genes and genomes %B Annu Rev Biophys Biomol Struct %V 29 %P 291-325 %! Comparative protein structure modeling of genes and genomes %M 10940251 %L 80 %F 80 %K Animals Comparative Study Computer Simulation Databases, Factual *Genome Genomics/*methods Humans Models, Biological Models, Genetic Models, Molecular Proteins/*chemistry Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. %X Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network. %Z 1056-8700 Journal Article Review %U http://salilab.org/pdf/Marti-Renom_AnnuRevBiophysBiomolStruct_2000.pdf %+ Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, Rockefeller University, New York, NY 10021, USA. %0 Book %A Marti-Renom, M. A. %A Yerkovich, B. %A Sali, A. %D 2003 %T Modeling protein structure from its sequence %B Current Protocols in Bioinformatics %I John Wiley & Sons, Inc., Hoboken, NJ %V V. 5 January Issue %P 5.1.1-5.1.32 %! Modeling protein structure from its sequence %L 118 %F 118 %U http://salilab.org/pdf/Marti-Renom_CurrProtBioinfo_2003.pdf %0 Book Section %A Marti-Renom, M.A. %A Fiser, A. %A Madhusudhan, M.S. %A John, B. %A Stuart, A.C. %A Eswar, N. %A Pieper, U. %A Shen, M-Y %A Sali, A. %D 2003 %T Modeling protein structure from its sequence %B Current Protocols in Bioinformatics %I John Wiley & Sons, Inc. %V V. 5 %P 5.1.1-5.1.32 %! Modeling protein structure from its sequence %L 126 %F 126 %Z November Issue %U http://salilab.org/pdf/Marti-Renom_CurrProtBioinfo_2003a.pdf %0 Book Section %A Marti-Renom, M.A. %A Yerkovich, B. %A Sali, A. %D 2002 %T Comparative protein structure prediction %B Current Protocols in Protein Science %I John Wiley & Sons %P 2.9.1-2.9.22 %! Comparative protein structure prediction %L 114 %F 114 %Z TY - JOUR %U http://salilab.org/pdf/Marti-Renom_CurrProtProtSci_2002.pdf %0 Journal Article %A Martinez-Avila, O. %A Wu, S. %A Kim, S.J. %A Cheng, Y. %A Khan, F. %A Samudrala, R. %A Sali, A. %A Horst, J. %A Habelitz, S. %D 2012 %T Self-assembly of Filamentous Amelogenin Requires Calcium and Phosphate: From Dimers via Nanoribbons to Fibrils %B Biomacromolecules %V 13 %P 3494-3502 %! Self-assembly of Filamentous Amelogenin Requires Calcium and Phosphate: From Dimers via Nanoribbons to Fibrils %2 PMCID3496023 %M 22974364;PMCID:PMC3496023 %L 283 %F 283 %U http://salilab.org/pdf/Martinez_Biomacromolecules_2012.pdf %0 Journal Article %A Martinez-Jimenez, F. %A Papadatos, G. %A Yang, L. %A Wallace, I.M. %A Kumar, V. %A Pieper, U. %A Sali, A. %A Brown, J.R. %A Overington, J.P. %A Marti-Renom, M.A. %D 2013 %T Target prediction for an open access set of compounds active against Mycobacterium tuberculosis %B PLoS Comp Biol %V 9 %N 10 %P e1003253 %7 2013 Oct 3 %! Target prediction for an open access set of compounds active against Mycobacterium tuberculosis %2 PMCID3789770 %M 24098102;PMCID:PMC3789770 %L 309 %F 309 %U http://salilab.org/pdf/Martinez-Jimenez_PLoSCompBiol_2013.pdf %0 Journal Article %A Mast, F. D. %A Fridy, P. C. %A Ketaren, N. E. %A Wang, J. %A Jacobs, E. Y. %A Olivier, J. P. %A Sanyal, T. %A Molloy, K. R. %A Schmidt, F. %A Rutkowska, M. %A Weisblum, Y. %A Rich, L. M. %A Vanderwall, E. R. %A Dambrauskas, N. %A Vigdorovich, V. %A Keegan, S. %A Jiler, J. B. %A Stein, M. E. %A Olinares, P. D. B. %A Herlands, L. %A Hatziioannou, T. %A Sather, D. N. %A Debley, J. S. %A Fenyö, D. %A Sali, A. %A Bieniasz, P. D. %A Aitchison, J. D. %A Chait, B. T. %A Rout, M. P. %D 2021 %T Highly synergistic combinations of nanobodies that target SARS-CoV-2 and are resistant to escape %B eLife %V 10 %P e73027 %! Highly synergistic combinations of nanobodies that target SARS-CoV-2 and are resistant to escape %R 10.7554/eLife.73027 %2 PMCID8043454 %M 33851164 %L 417 %F 417 %X Despite the great promise of vaccines, the COVID-19 pandemic is ongoing and future serious outbreaks are highly likely, so that multi-pronged containment strategies will be required for many years. Nanobodies are the smallest naturally occurring single domain antigen binding proteins identified to date, possessing numerous properties advantageous to their production and use. We present a large repertoire of high affinity nanobodies against SARS-CoV-2 Spike protein with excellent kinetic and viral neutralization properties, which can be strongly enhanced with oligomerization. This repertoire samples the epitope landscape of the Spike ectodomain inside and outside the receptor binding domain, recognizing a multitude of distinct epitopes and revealing multiple neutralization targets of pseudoviruses and authentic SARS-CoV-2, including in primary human airway epithelial cells. Combinatorial nanobody mixtures show highly synergistic activities, and are resistant to mutational escape and emerging viral variants of concern. These nanobodies establish an exceptional resource for superior COVID-19 prophylactics and therapeutics.Competing Interest StatementThe authors have declared no competing interest. %U https://salilab.org/pdf/Mast_eLife_2021.pdf %0 Journal Article %A Mast, F. D. %A Navare, A. T. %A van der Sloot, A. M. %A Coulombe-Huntington, J. %A Rout, M. P. %A Baliga, N. S. %A Kaushansky, A. %A Chait, B. T. %A Aderem, A. %A Rice, C. M. %A Sali, A. %A Tyers, M. %A Aitchison, J. D. %D 2020 %T Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality %B J Cell Biol %V 219 %N 10 %8 Oct 5 %! Crippling life support for SARS-CoV-2 and other viruses through synthetic lethality %@ 0021-9525 %R 10.1083/jcb.202006159 %2 PMCID7659715 %M 32785687 %L 404 %F 404 %X With the rapid global spread of SARS-CoV-2, we have become acutely aware of the inadequacies of our ability to respond to viral epidemics. Although disrupting the viral life cycle is critical for limiting viral spread and disease, it has proven challenging to develop targeted and selective therapeutics. Synthetic lethality offers a promising but largely unexploited strategy against infectious viral disease; as viruses infect cells, they abnormally alter the cell state, unwittingly exposing new vulnerabilities in the infected cell. Therefore, we propose that effective therapies can be developed to selectively target the virally reconfigured host cell networks that accompany altered cellular states to cripple the host cell that has been converted into a virus factory, thus disrupting the viral life cycle. %U https://salilab.org/pdf/Mast_JCellBiol_2020.pdf %+ Center for Global Infectious Disease Research, Seattle Children's Research Institute, Seattle, WA. Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Canada. Laboratory of Cellular and Structural Biology, The Rockefeller University, New York, NY. Institute for Systems Biology, Seattle, WA. Department of Pediatrics, University of Washington, Seattle, WA. Laboratory of Mass Spectrometry and Gaseous Ion Chemistry, The Rockefeller University, New York, NY. Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY. Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA. Department of Biochemistry, University of Washington, Seattle, WA. %G eng %0 Journal Article %A Matsumoto, R. %A Sali, A. %A Ghildyal, N. %A Karplus, M. %A Stevens, R. L. %D 1995 %T Packaging of proteases and proteoglycans in the granules of mast cells and other hematopoietic cells. A cluster of histidines on mouse mast cell protease 7 regulates its binding to heparin serglycin proteoglycans %B J Biol Chem %V 270 %N 33 %P 19524-19531 %8 Aug 18 %! Packaging of proteases and proteoglycans in the granules of mast cells and other hematopoietic cells. A cluster of histidines on mouse mast cell protease 7 regulates its binding to heparin serglycin proteoglycans %M 7642636 %L 40 %F 40 %K Amino Acid Sequence Animals Base Sequence *Bone Marrow Cells Cell Line Cytoplasmic Granules/enzymology/metabolism Heparin/*metabolism Histidine/chemistry/*metabolism Mast Cells/enzymology/*metabolism Mice Mice, Inbred BALB C Molecular Sequence Data Protein Binding Proteoglycans/chemistry/*metabolism Recombinant Proteins/chemistry/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid Serine Endopeptidases/chemistry/*metabolism Spodoptera %X Mouse mast cell protease 7 (mMCP-7) is a tryptase stored in the secretory granules of mast cells. At the granule pH of 5.5, mMCP-7 is fully active and is bound to heparin-containing serglycin proteoglycans. to understand the interaction of mMCP-7 with heparin inside and outside the mast cell, this trytase was first studied by comparative protein modeling. The "pro" form of mMCP-7 was then expressed in insect cells and studied by site-directed mutagenesis. Although mMCP-7 lacks known linear sequences of amino acis that interact with heparin, the three-dimensional model of mMCP-7 revealed an area on the surface of the folded protein away from the substrate-binding site that exhibits a strong positive electrostatic potential at the acidic pH of the granule. In agreement with this calculation, recombinant pro-mMCP-7 bound to a heparin-affinity column at pH 5.5 and readily dissociated from the column at pH > 6.5. Site-directed mutagenesis confirmed the prediction that the conversion of His residues 8,68, and 70 in the positively charged region into Glu prevents the binding of pro-mMCP-7 to heparin. Because the binding requires positively charged His residues, native mMCP-7 is able to dissociate from the protease/proteoglycan macromolecular complex when the complex is exocytosed from bone marrow-derived mast cells into a neutral pH environment. Many hematopoietic effector cells store positively charged proteins in granules that contain serglycin proteoglycans. The heparin/mMCP-7 interaction, which depends on the tertiary structure of the tryptase, may be representative of a general control mechanism by which hematopoietic cells maximize storage of properly folded, enzymatically active proteins in their granules. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Matsumoto_JBiolChem_1995.pdf %+ Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA. %0 Journal Article %A Maurer, S. M. %A Rai, A. %A Sali, A. %D 2004 %T Finding Cures for Tropical Diseases: Is Open Source an Answer? %B PLoS Medicine %V 1 %N 3 %P e56 %8 Dec %! Finding Cures for Tropical Diseases: Is Open Source an Answer? %M 15630466 %L 141 %F 141 %Z 1549-1277 Journal article %U http://salilab.org/pdf/Maurer_PLoSMedicine_2004.pdf %0 Journal Article %A Maurer, S. M. %A Rai, A. %A Sali, A. %D 2004 %T Finding Cures for Tropical Diseases: Is Open Source An Answer? %B Minnesota Journal of Law, Science & Technology %V 6 %P 169-175 %! Finding Cures for Tropical Diseases: Is Open Source An Answer? %L 134 %F 134 %U http://salilab.org/pdf/Maurer_MinnesotaJournalofLaw_2004.pdf %0 Book Section %A Maurer, S.M. %A Rai, A. %A Sali, A. %D 2004 %T Finding Cures for Tropical Diseases: Is Open Source An Answer? %E Laboratory, Lynn Yarris %B Biotechnology: Essays From Its Heartland %V June 2004 %P 33-37 %! Finding Cures for Tropical Diseases: Is Open Source An Answer? %L 136 %F 136 %U http://salilab.org/pdf/Maurer_Biotechnology_2004.pdf %0 Journal Article %A McMahon, SA %A Miller, JL %A Lawton, JA %A Kerkow, DE %A Hodes, A %A Marti-Renom, MA %A Doulatov, S %A Narayanan, E %A Sali, A %A Miller, JF %A Ghosh, P %D 2005 %T The C-type lectin fold as an evolutionary solution for massive sequence variation %B Nature Structural Molecular Biology %V 12 %N 10 %P 886-892 %8 Oct %! The C-type lectin fold as an evolutionary solution for massive sequence variation. %@ 1545-9993 %M 16170324 %L 156 %F 156 %K Amino Acid Sequence Bacterial Outer Membrane Proteins Bacteriophages Bordetella Evolution, Molecular Genetic Variation Genome, Viral Lectins, C-Type Molecular Sequence Data Protein Conformation Protein Folding Viral Proteins Virulence Factors, Bordetella %X Only few instances are known of protein folds that tolerate massive sequence variation for the sake of binding diversity. The most extensively characterized is the immunoglobulin fold. We now add to this the C-type lectin (CLec) fold, as found in the major tropism determinant (Mtd), a retroelement-encoded receptor-binding protein of Bordetella bacteriophage. Variation in Mtd, with its approximately 10(13) possible sequences, enables phage adaptation to Bordetella spp. Mtd is an intertwined, pyramid-shaped trimer, with variable residues organized by its CLec fold into discrete receptor-binding sites. The CLec fold provides a highly static scaffold for combinatorial display of variable residues, probably reflecting a different evolutionary solution for balancing diversity against stability from that in the immunoglobulin fold. Mtd variants are biased toward the receptor pertactin, and there is evidence that the CLec fold is used broadly for sequence variation by related retroelements. %U http://salilab.org/pdf/McMahon_NatureStructuralMolecularBiology_2005.pdf %+ Department of Chemistry & Biochemistry, University of California at San Diego, La Jolla, California 92093, USA. %G eng %0 Journal Article %A Medema, M.H. %A Cimermancic, P. %A Sali, A. %A Takano, E. %A Fischbach, M. %D 2014 %T A systematic computational analysis of biosynthetic gene cluster evolution: Lessons for engineering biosynthesis %B PLoS Comput Biol %V 10 %P e1004016 %! A systematic computational analysis of biosynthetic gene cluster evolution: Lessons for engineering biosynthesis %2 PMCID4256081 %M 25474254;PMCID:PMC4256081 %L 328 %F 328 %U http://salilab.org/pdf/Medema_PLoSComputBiol_2014.pdf %0 Journal Article %A Melo, F. %A Sali, A. %D 2007 %T Fold assessment for comparative protein structure modeling %B Protein Sci %V 16 %N 11 %P 2412-2426 %8 Nov %! Fold assessment for comparative protein structure modeling %@ 0961-8368 (Print) %M 17905832 %L 186 %F 186 %K Bayes Theorem Databases, Protein False Positive Reactions Models, Statistical Molecular Conformation Multivariate Analysis Protein Binding Protein Conformation *Protein Folding Protein Structure, Tertiary Proteins/*chemistry Proteomics/methods Reproducibility of Results Sensitivity and Specificity Software Stereoisomerism %X Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences. %Z R01 GM54762/GM/United States NIGMS U54 GM62529/GM/United States NIGMS Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't United States a publication of the Protein Society %U http://salilab.org/pdf/Melo_ProteinSci_2007.pdf %+ Departamento de Genetica Molecular y Microbiologia, Facultad de Ciencias Biologicas, Pontificia Universidad Catolica de Chile, Santiago, Chile. fmelo@bio.puc.cl %G eng %0 Journal Article %A Melo, F. %A Sanchez, R. %A Sali, A. %D 2002 %T Statistical potentials for fold assessment %B Protein Sci %V 11 %N 2 %P 430-448 %! Statistical potentials for fold assessment %@ 0961-8368 %R 10.1110/ps.22802 %M 11790853 %L 105 %F 105 %X A protein structure model generally needs to be evaluated to assess whether or not it has the correct fold. To improve fold assessment, four types of a residue-level statistical potential were optimized, including distance-dependent, contact, Phi/Psi dihedral angle, and accessible surface statistical potentials. Approximately 10,000 test models with the correct and incorrect folds were built by automated comparative modeling of protein sequences of known structure. The criterion used to discriminate between the correct and incorrect models was the Z-score of the model energy. The performance of a Z-score was determined as a function of many variables in the derivation and use of the corresponding statistical potential. The performance was measured by the fractions of the correctly and incorrectly assessed test models. The most discriminating combination of any one of the four tested potentials is the sum of the normalized distance-dependent and accessible surface potentials. The distance-dependent potential that is optimal for assessing models of all sizes uses both C-alpha and C-beta atoms as interaction centers, distinguishes between all 20 standard residue types, has the distance range of 30 Angstrom, and is derived and used by taking into account the sequence separation of the interacting atom pairs. The terms for the sequentially local interactions are significantly less informative than those for the sequentially nonlocal interactions. The accessible surface potential that is optimal for assessing models of all sizes uses C-beta atoms as interaction centers and distinguishes between all 20 standard residue types. The performance of the tested statistical potentials is not likely to improve significantly with an increase in the number of known protein structures used in their derivation. The parameters of fold assessment whose optimal values vary significantly with model size include the size of the known protein structures used to derive the potential and the distance range of the accessible surface potential. Fold assessment by statistical potentials is most difficult for the very small models. This difficulty presents a challenge to fold assessment in large-scale comparative modeling, which produces many small and incomplete models. The results described in this study provide a basis for an optimal use of statistical potentials in fold assessment. %U http://salilab.org/pdf/Melo_ProteinSci_2002.pdf %0 Journal Article %A Mirkovic, N. %A Marti-Renom, M. A. %A Weber, B. L. %A Sali, A. %A Monteiro, A. N. %D 2004 %T Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition %B Cancer Res %V 64 %N 11 %P 3790-3797 %8 Jun 1 %! Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition %M 15172985 %L 129 %F 129 %K BRCA1 Protein/*chemistry/genetics Breast Neoplasms/*genetics Female *Genes, BRCA1 Genetic Predisposition to Disease Humans *Mutation, Missense Ovarian Neoplasms/*genetics Pedigree Protein Conformation Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Structure-Activity Relationship Trans-Activation (Genetics) %X The BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer. %Z 0008-5472 Journal Article %U http://salilab.org/pdf/Mirkovic_CancerRes_2004.pdf %+ Laboratory of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, Rockefeller University, New York, New York, USA. %0 Journal Article %A Miwa, J. M. %A Ibanez-Tallon, I. %A Crabtree, G. W. %A Sanchez, R. %A Sali, A. %A Role, L. W. %A Heintz, N. %D 1999 %T lynx1, an endogenous toxin-like modulator of nicotinic acetylcholine receptors in the mammalian CNS %B Neuron %V 23 %N 1 %P 105-114 %! lynx1, an endogenous toxin-like modulator of nicotinic acetylcholine receptors in the mammalian CNS %@ 0896-6273 %M 10402197 %L 73 %F 73 %X Elapid snake venom neurotoxins exert their effects through high-affinity interactions with specific neurotransmitter receptors. A novel murine gene, lynx1, is highly expressed in the brain and contains the cysteine-rich motif characteristic of this class of neurotoxins. Primary sequence and gene structure analyses reveal an evolutionary relationship between lynx1 and the Ly-6/neurotoxin gene family, lynx1 is expressed in large projection neurons in the hippocampus, cortex, and cerebellum. In cerebellar neurons, lynx1 protein is localized to a specific subdomain including the soma and proximal dendrites. lynx1 binding to brain sections correlates with the distribution of nAChRs, and application of lynx1 to Xenopus oocytes expressing nAChRs results in an increase in acetylcholine-evoked macroscopic currents. These results identify lynx1 as a novel protein modulator for nAChRs in vitro, which could have important implications in the regulation of cholinergic function in vivo. %U http://salilab.org/pdf/Miwa_Neuron_1999.pdf %0 Journal Article %A Molnar, K. %A Bonomi, M. %A Pellarin, R. %A Clinthorne, G. %A Gonzalez, G. %A Goldberg, S. %A Goulian, M. %A Sali, A. %A DeGrado, W. %D 2014 %T Cys-Scanning Disulfide Crosslinking and Bayesian Modeling Probe the Transmembrane Signaling Mechanism of the Histidine Kinase, PhoQ %B Structure %V 22 %P 1239-1251 %! Cys-Scanning Disulfide Crosslinking and Bayesian Modeling Probe the Transmembrane Signaling Mechanism of the Histidine Kinase, PhoQ %2 PMCID4322757 %M 25087511;PMCID:PMC4322757 %L 318 %F 318 %U http://salilab.org/pdf/Molnar_Structure_2014.pdf %0 Journal Article %A Morin, A. %A Urban, J. %A Adams, P.D. %A Foster, I. %A Sali, A. %A Baker, D. %A Sliz, P. %D 2012 %T Shining Light into Black Boxes %B Science %V 336 %P 159-160 %! Shining Light into Black Boxes %2 PMCID4203337 %M 22499926;PMCID:PMC4203337 %L 279 %F 279 %U http://salilab.org/pdf/Morin_Science_2012.pdf %0 Journal Article %A Nagata, T. %A Gupta, V. %A Sorce, D. %A Kim, W. Y. %A Sali, A. %A Chait, B. T. %A Shigesada, K. %A Ito, Y. %A Werner, M. H. %D 1999 %T Immunoglobulin motif DNA recognition and heterodimerization of the PEBP2/CBF Runt domain %B Nat Struct Biol %V 6 %N 7 %P 615-619 %! Immunoglobulin motif DNA recognition and heterodimerization of the PEBP2/CBF Runt domain %@ 1072-8368 %M 10404214 %L 72 %F 72 %X The polyomavirus enhancer binding protein 2 (PEBP2) or core binding factor (CBF) is a heterodimeric enhancer binding protein that is associated with genetic regulation of hematopoiesis and osteogenesis. Aberrant forms of PEBP2/CBF are implicated in the cause of the acute human leukemias and in a disorder of bone development known as cleidocranial dysplasia. The common denominator in the natural and mutant forms of this protein is a highly conserved domain of PEBP2/CBF alpha, termed the Runt domain (RD), which is responsible for both DNA binding and heterodimerization with the beta subunit of PEBP2/CBF. The three-dimensional structure of the RD bound to DNA has been determined to be an S-type immunoglobulin fold, establishing a structural relationship between the RD and the core DNA binding domains of NF-kappa B, NFAT1, p53 and the STAT proteins, NMR spectroscopy of a 43.6 kD RD-beta-DNA ternary complex identified the surface of the RD in contact with the beta subunit, suggesting a mechanism for the enhancement of RD DNA binding by beta. Analysis of leukemogenic mutants within the RD provides molecular insights into the role of this factor in leukemogenesis and cleidocranial dysplasia. %U http://salilab.org/pdf/Nagata_NatStructBiol_1999.pdf %0 Journal Article %A Nguyen, T. D. %A Gow, J. M. %A Chinn, L. W. %A Kelly, L. %A Jeong, H. %A Huang, C. C. %A Stryke, D. %A Kawamoto, M. %A Johns, S. J. %A Carlson, E. %A Taylor, T. %A Ferrin, T. E. %A Sali, A. %A Giacomini, K. M. %A Kroetz, D. L. %D 2006 %T PharmGKB submission update: IV. PMT submissions of genetic variations in ATP-Binding cassette transporters to the PharmGKB network %B Pharmacol Rev %V 58 %N 1 %P 1-2 %8 Mar %! PharmGKB submission update: IV. PMT submissions of genetic variations in ATP-Binding cassette transporters to the PharmGKB network %M 16507877 %L 168 %F 168 %K ATP-Binding Cassette Transporters/*genetics/metabolism Humans Molecular Sequence Data Pharmacogenetics *Variation (Genetics) %Z 0031-6997 (Print) Journal Article Research Support, N.I.H., Extramural %U http://salilab.org/pdf/Nguyen_PharmacolRev_2006.pdf %+ Department of Biopharmaceutical Sciences, University of California, San Francisco, USA. %0 Journal Article %A Nickell, S. %A Beck, F. %A Scheres, S.H.W. %A Korinek, A. %A Forster, F. %A Lasker, K. %A Mihalache, O. %A Sun, N. %A Nagy, I. %A Sali, A. %A Plitzko, J. %A Carazo, J.-M. %A Mann, M. %A Baumeister, W. %D 2009 %T Insights into the Molecular Architecture of the 26S Proteasome %B Proc Natl Acad Sci U S A %V 29 %N 104 %P 11943-11947 %! Insights into the Molecular Architecture of the 26S Proteasome %2 PMCID2715492 %M 19581588;PMCID:PMC2715492 %L 225 %F 225 %U http://salilab.org/pdf/Nickell_ProcNatlAcadSciUSA_2009.pdf %0 Journal Article %A Opoku-Nsiah, KA %A de la Pena, AH %A Williams, SK %A Chopra, N %A Sali, A %A Lander, GC %A Gestwicki, JE %D 2022 %T The YΦ Motif Defines the Structure-Activity Relationships of Human 20S Proteasome Activators %B Nat Commun %V 13 %N 1 %P 1226 %! The YΦ Motif Defines the Structure-Activity Relationships of Human 20S Proteasome Activators %R 10.1038/s41467-022-28864-x %2 PMCID8907193 %M 35264557 %L 436 %F 436 %U https://salilab.org/pdf/Opoku_NatCommun_2022.pdf %0 Journal Article %A Orti, L %A Carbajo, RJ %A Pieper, U %A Eswar, N %A Maurer, SM %A Rai, AK %A Taylor, G %A Todd, MH %A Pineda-Lucena, A %A Sali, A %A Marti-Renom, MA %D 2009 %T A kernel for open source drug discovery in tropical diseases %B PLoS Negl Trop Dis %V 3 %N 4 %P e418 %! A kernel for open source drug discovery in tropical diseases. %@ 1935-2735 %2 PMCID2667270 %M 19381286;PMCID: PMC2667270 %L 220 %F 220 %X BACKGROUND: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". METHODOLOGY/PRINCIPAL FINDINGS: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. CONCLUSIONS/SIGNIFICANCE: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases. %U http://salilab.org/pdf/Orti_PLoSNeglectedTropicalDiseases_2009.pdf %+ Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigaci‚àö√µn Pr‚àö√•ncipe Felipe, Valencia, Spain. %G eng %0 Journal Article %A Orti, L %A Carbajo, RJ %A Pieper, U %A Eswar, N %A Maurer, SM %A Rai, AK %A Taylor, G %A Todd, MH %A Pineda-Lucena, A %A Sali, A %A Marti-Renom, MA %D 2009 %T A kernel for the Tropical Disease Initiative %B Nat Biotechnol %V 27 %N 4 %P 320-321 %8 Apr %! A kernel for the Tropical Disease Initiative. %@ 1546-1696 %2 PMCID3088649 %M 19352362;PMCID:PMC3088649 %L 223 %F 223 %U http://salilab.org/pdf/Orti_NatBiotechnol_2009.pdf %+ Structural Genomics Unit, Bioinformatics and Genomics Department, Centro de Investigaci‚àö√µn Pr‚àö√•ncipe Felipe, Valencia, Spain. %G eng %0 Journal Article %A Otsuka, S. %A Tempkin, J.O.B. %A Zhang, W. %A Politi, A.Z. %A Rybina, A. %A Hossain, M.J. %A Kueblbeck, M. %A Callegari, A. %A Koch, B. %A Morero, N.R. %A Sali, A. %A Ellenberg, J. %D 2023 %T A quantitative map of nuclear pore assembly reveals two distinct mechanisms %B Nature %V 613 %N 7944 %P 575-581 %! A quantitative map of nuclear pore assembly reveals two distinct mechanisms %R 10.1038/s41586-022-05528-w %2 PMCID9849139 %M 36599981 %L 440 %F 440 %U https://salilab.org/pdf/Otsuka_Nature_2023.pdf %0 Journal Article %A Overington, J. P. %A Zhu, Z. Y. %A Sali, A. %A Johnson, M. S. %A Sowdhamini, R. %A Louie, G. V. %A Blundell, T. L. %D 1993 %T Molecular recognition in protein families: a database of aligned three-dimensional structures of related proteins %B Biochem Soc Trans %V 21 ( Pt 3) %N 3 %P 597-604 %8 Aug %! Molecular recognition in protein families: a database of aligned three-dimensional structures of related proteins %M 8224474 %L 28 %F 28 %K Amino Acid Sequence Animals Binding Sites Carrier Proteins/chemistry Comparative Study *Databases, Factual Hydroxymethylbilane Synthase/chemistry Lactoferrin/chemistry Models, Molecular Molecular Sequence Data *Protein Conformation Protein Folding *Protein Structure, Secondary Proteins/*chemistry/metabolism Sequence Homology, Amino Acid Transferrin/chemistry %Z 0300-5127 Journal Article %U http://salilab.org/pdf/Overington_BiochemSocTrans_1993.pdf %+ Department of Crystallography, Birkbeck College, University of London, U.K. %0 Journal Article %A Overington, J.P. %A Johnson, M.S. %A Topham, C. %A McLeod, A. %A Sali, A. %A Zhu, Z.Y. %A Sibanda, L. %A Blundell, T.L. %D 1990 %T Applications of environment specific amino acid substitution tables to identification of key residues in protein tertiary structure %B Curr Sci %V 59 %P 867-874 %8 1990/// %! Applications of environment specific amino acid substitution tables to identification of key residues in protein tertiary structure %L 15 %F 15 %K Amino Acid Substitution %Z TY - JOUR %U http://salilab.org/pdf/Overington_CurrSci_1990.pdf %0 Journal Article %A Overington, J. %A Donnelly, D. %A Johnson, M. S. %A Sali, A. %A Blundell, T. L. %D 1992 %T Environment-specific amino acid substitution tables: tertiary templates and prediction of protein folds %B Protein Sci %V 1 %N 2 %P 216-226 %8 Feb %! Environment-specific amino acid substitution tables: tertiary templates and prediction of protein folds %M 1304904 %L 26 %F 26 %K Amino Acid Sequence Amino Acids/*chemistry/genetics Comparative Study Conserved Sequence Databases, Factual Mathematical Computing Molecular Sequence Data Pattern Recognition, Automated Probability *Protein Folding *Protein Structure, Tertiary Proteins/*chemistry/genetics Reference Values Research Support, Non-U.S. Gov't Sequence Alignment/*methods Sequence Homology, Amino Acid %X The local environment of an amino acid in a folded protein determines the acceptability of mutations at that position. In order to characterize and quantify these structural constraints, we have made a comparative analysis of families of homologous proteins. Residues in each structure are classified according to amino acid type, secondary structure, accessibility of the side chain, and existence of hydrogen bonds from the side chains. Analysis of the pattern of observed substitutions as a function of local environment shows that there are distinct patterns, especially for buried polar residues. The substitution data tables are available on diskette with Protein Science. Given the fold of a protein, one is able to predict sequences compatible with the fold (profiles or templates) and potentially to discriminate between a correctly folded and misfolded protein. Conversely, analysis of residue variation across a family of aligned sequences in terms of substitution profiles can allow prediction of secondary structure or tertiary environment. %Z 0961-8368 Journal Article %U http://salilab.org/pdf/Overington_ProteinSci_1992.pdf %+ Department of Crystallography, Birkbeck College, University of London, UK. %0 Journal Article %A Overington, J. %A Johnson, M. S. %A Sali, A. %A Blundell, T. L. %D 1990 %T Tertiary structural constraints on protein evolutionary diversity: templates, key residues and structure prediction %B Procedures in Biological Science %V 241 %N 1301 %P 132-145 %8 Aug 22 %! Tertiary structural constraints on protein evolutionary diversity: templates, key residues and structure prediction %M 1978340 %L 14 %F 14 %K Amino Acid Sequence Animals *Evolution Humans Molecular Sequence Data Probability *Protein Conformation Proteins/*genetics Research Support, Non-U.S. Gov't *Variation (Genetics) %X The pattern of residue substitution in divergently evolving families of globular proteins is highly variable. At each position in a fold there are constraints on the identities of amino acids from both the three-dimensional structure and the function of the protein. To characterize and quantify the structural constraints, we have made a comparative analysis of families of homologous globular proteins. Residues are classified according to amino acid type, secondary structure, accessibility of the sidechain, and existence of hydrogen bonds from sidechain to other sidechains or peptide carbonyl or amide functions. There are distinct patterns of substitution especially where residues are both solvent inaccessible and hydrogen bonded through their sidechains. The patterns of residue substitution can be used to construct templates or to identify 'key' residues if one or more structures are known. Conversely, analysis of conversation and substitution across a large family of aligned sequences in terms of substitution profiles can allow prediction of tertiary environment or indicate a functional role. Similar analyses can be used to test the validity of putative structures if several homologous sequences are available. %Z 0962-8452 Journal Article %U http://salilab.org/pdf/Overington_ProceduresinBiologicalScience_1990.pdf %+ Department of Crystallography, Birkbeck College, University of London, U.K. %0 Journal Article %A Padavannil, A %A Sarkar, P %A Kim, SJ %A Cagatay, T %A Jiou, J %A Brautigam, CA %A Tomchick, DR %A Sali, A %A D'Arcy, S %A Chook, YM %D 2019 %T Importin-9 wraps around the H2A-H2B core to act as nuclear importer and histone chaperone %B eLife %V 8 %P e43630 %! Importin-9 wraps around the H2A-H2B core to act as nuclear importer and histone chaperone %R 10.7554/eLife.43630 %2 PMCID6453568 %M 30855230 %L 393 %F 393 %U https://salilab.org/pdf/Padavannil_eLife_2019.pdf %0 Journal Article %A Pandey, K.C. %A Barkan, D. T. %A Sali, A. %A Rosenthal, P.J. %D 2009 %T Regulatory elements within the prodomain of falcipain-2, a cysteine protease of the malaria parasite Plasmodium falciparum %B PLoS One %V 4(5):e5694 %! Regulatory elements within the prodomain of falcipain-2, a cysteine protease of the malaria parasite Plasmodium falciparum %2 PMCID2682653 %M 19479029;PMCID:PMC2682653 %L 221 %F 221 %U http://salilab.org/pdf/Pandey_PLoSOne_2009.pdf %0 Journal Article %A Pandya, C. %A Brown, S. %A Pieper, U. %A Sali, A. %A Dunaway-Mariano, D. %A Babbitt, P. %A Xia, Y. %A Allen, K. %D 2013 %T The consequences of domain insertion on sequence-structure divergence in a superfold %B Proc Natl Acad Sci USA %V 110 %P 3381-3387 %! The consequences of domain insertion on sequence-structure divergence in a superfold %2 PMCID3767544 %M 23959887;PMCID:PMC3767544 %L 306 %F 306 %U http://salilab.org/pdf/Pandya_ProcNatlAcadSciUSA_2013.pdf %0 Journal Article %A Pathare, G. %A Nagy, I. %A Bohn, S. %A Unverdorben, P. %A Hubert, A. %A Korner, R. %A Nickell, S. %A Lasker, K. %A Sali, A. %A Tamura, T. %A Nishioka, T. %A Forster, F. %A Baumeister, W. %A Bracher, A. %D 2012 %T The proteasomal subunit Rpn6 is a molecular clamp holding the core and regulatory subcomplexes together %B Proc Natl Acad Sci USA %V 109 %P 149-154 %! The proteasomal subunit Rpn6 is a molecular clamp holding the core and regulatory subcomplexes together %2 PMCID3252951 %M 22187461;PMCID:PMC3252951 %L 273 %F 273 %U http://salilab.org/pdf/Pathare_ProcNatlAcadSciUSA_2012.pdf %0 Journal Article %A Pedersen, B. %A Kumar, H. %A Waight, A. %A Risenmay, A. %A Roe-Zurz, Z. %A Chau, B. %A Schlessinger, A. %A Bonomi, M. %A Harries, W. %A Sali, A. %A Johri, A. %A Stroud, R. %D 2013 %T Crystal structure of a eukaryotic phosphate transporter %B Nature %V 496 %P 533-536 %! Crystal structure of a eukaryotic phosphate transporter %2 PMCID3678552 %M 23542591;PMCID:PMC3678552 %L 294 %F 294 %U http://salilab.org/pdf/Pedersen_Nature_2013.pdf %0 Journal Article %A Peterson, M.E. %A Chen, F. %A Saven, J.G. %A Roos, D.S. %A Babbitt, P. C. %A Sali, A. %D 2009 %T Evolutionary constraints on structural similarity in orthologs and paralogs %B Protein Sci %V 18 %P 1306-1315 %! Evolutionary constraints on structural similarity in orthologs and paralogs %2 PMCID2774440 %M 19472362;PMCID:PMC2774440 %L 217 %F 217 %U http://salilab.org/pdf/Peterson_ProteinSci_2009.pdf %0 Journal Article %A Peulen, T.O. %A Hemmen, K. %A Greife, A. %A Webb, B.M. %A Felekyan, S. %A Sali, A. %A Seidel, C.A.M. %A Sanabria, H. %A Heinze, K.G. %D 2024 %T tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling %B Bioinformatics, in press %! tttrlib: modular software for integrating fluorescence spectroscopy, imaging, and molecular modeling %L 460 %F 460 %0 Journal Article %A Peulen, T.O. %A Sali, A. %D 2023 %T Bayesian Fluorescence Framework for integrative modeling of biomolecules %B in press %! Bayesian Fluorescence Framework for integrative modeling of biomolecules %R 10.1101/2023.10.26.564048 %L 446 %F 446 %0 Journal Article %A Pieper, U. %A Eswar, N. %A Braberg, H. %A Madhusudhan, M. S. %A Davis, F. P. %A Stuart, A. C. %A Mirkovic, N. %A Rossi, A. %A Marti-Renom, M. A. %A Fiser, A. %A Webb, B. %A Greenblatt, D. %A Huang, C. C. %A Ferrin, T. E. %A Sali, A. %D 2004 %T MODBASE, a database of annotated comparative protein structure models, and associated resources %B Nucleic Acids Res %V 32 %P D217-D222 %! MODBASE, a database of annotated comparative protein structure models, and associated resources %@ 0305-1048 %R 10.1093/nar/gkh095 %M 14681398 %L 135 %F 135 %X MODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on. the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/saillab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1 262 629 models for domains in 659 495 out of 1182126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb). %Z SI %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2004.pdf %0 Journal Article %A Pieper, U. %A Eswar, N. %A Davis, F. P. %A Braberg, H. %A Madhusudhan, M. S. %A Rossi, A. %A Marti-Renom, M. %A Karchin, R. %A Webb, B. M. %A Eramian, D. %A Shen, M. Y. %A Kelly, L. %A Melo, F. %A Sali, A. %D 2006 %T MODBASE: a database of annotated comparative protein structure models and associated resources %B Nucleic Acids Res %V 34 %N Database issue %P D291-295 %8 Jan 1 %! MODBASE: a database of annotated comparative protein structure models and associated resources %M 16381869 %L 160 %F 160 %K Binding Sites *Databases, Protein Humans Internet Ligands *Models, Molecular Polymorphism, Single Nucleotide Protein Structure, Tertiary Proteins/*chemistry/genetics/metabolism Software *Structural Homology, Protein Systems Integration User-Computer Interface %X MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models for all available protein sequences that can be matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, and improvements in the software for calculating the models. MODBASE currently contains 3 094 524 reliable models for domains in 1 094 750 out of 1 817 889 unique protein sequences in the UniProt database (July 5, 2005); only models based on statistically significant alignments and models assessed to have the correct fold despite insignificant alignments are included. MODBASE also allows users to generate comparative models for proteins of interest with the automated modeling server MODWEB (http://salilab.org/modweb). Our other resources integrated with MODBASE include comprehensive databases of multiple protein structure alignments (DBAli, http://salilab.org/dbali), structurally defined ligand binding sites and structurally defined binary domain interfaces (PIBASE, http://salilab.org/pibase) as well as predictions of ligand binding sites, interactions between yeast proteins, and functional consequences of human nsSNPs (LS-SNP, http://salilab.org/LS-SNP). %Z 1362-4962 (Electronic) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2006.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, QB3 at Mission Bay, Office 503B, University of California at San Francisco 1700 4th Street, San Francisco, CA 94158, USA. %0 Journal Article %A Pieper, U. %A Eswar, N. %A Stuart, A. C. %A Ilyin, V. A. %A Sali, A. %D 2002 %T MODBASE, a database of annotated comparative protein structure models %B Nucleic Acids Res %V 30 %N 1 %P 255-259 %! MODBASE, a database of annotated comparative protein structure models %@ 0305-1048 %M 11752309 %L 110 %F 110 %X MODBASE (http://guitar.rockefeller.edu/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on PSI-BLAST, IMPALA and MODELLER. MODBASE uses the MySQL relational database management system for flexible and efficient querying, and the MODVIEW Netscape plugin for viewing and manipulating multiple sequences and structures. It is updated regularly to reflect the growth of the protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different datasets. The largest dataset contains models for domains in 304 517 out of 539 171 unique protein sequences in the complete TrEMBL database (23 March 2001); only models based on significant alignments (PSI-BLAST E-value < 10(-4)) and models assessed to have the correct fold are included. Other datasets include models for target selection and structure-based annotation by the New York Structural Genomics Research Consortium, models for prediction of genes in the Drosophila melanogaster genome, models for structure determination of several ribosomal particles and models calculated by the MODWEB comparative modeling web server. %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2002.pdf %0 Journal Article %A Pieper, U. %A Schlessinger, A. %A Kloppmann, E. %A Chang, G.A. %A Chou, J.J. %A Dumont, M. %A Fox, B. %A Fromme, P. %A Hendrickson, W. %A Malkowski, M. %A Rees, D. %A Stokes, D. %A Stowell, M.H.B. %A Wiener, M. %A Rost, B. %A Stroud, R. %A Stevens, R. %A Sali, A. %D 2013 %T Coordinating the impact of structural genomics on the human a-helical transmembrane proteome %B Nat Struct Mol Biol %V 20 %P 135-138 %! Coordinating the impact of structural genomics on the human a-helical transmembrane proteome %2 PMCID3645303 %M 23381628;PMCID:PMC3645303 %L 291 %F 291 %U http://salilab.org/pdf/Pieper_NatStructMolBiol_2013.pdf %0 Journal Article %A Pieper, U. %A Webb, B.M. %A Barkan, D.T. %A Schneidman-Duhovny, D. %A Schlessinger, A. %A Braberg, H. %A Yang, Z. %A Meng, E.C. %A Pettersen, E.F. %A Huang, C.C. %A Datta, R.S. %A Sampathkumar, P. %A Madhusudhan, M.S. %A Sjolander, K. %A Ferrin, T.E. %A Burley, S.K. %A Sali, A %D 2011 %T ModBase, a database of annotated comparative protein structure models, and associated resources %B Nucleic Acids Research %V 39 %P 465-474 %! ModBase, a database of annotated comparative protein structure models, and associated resources %2 PMCID3013688 %M 21097780;PMCID:PMC3013688 %L 250 %F 250 %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2010.pdf %0 Journal Article %A Pieper, U. %A Webb, B. %A Dong, G.Q. %A Schneidman-Duhovny, D. %A Fan, H. %A Kim, S.J. %A Khuri, N. %A Spill, Y. %A Weinkam, P. %A Hammel, M. %A Tainer, J. %A Nilges, M. %A Sali, A. %D 2014 %T ModBase, a database of annotated comparative protein structure models, and associated resources %B Nucleic Acids Res %V 42 %P 336-346 %! ModBase, a database of annotated comparative protein structure models, and associated resources %2 PMCID3965011 %M 24271400;PMCID:PMC3965011 %L 317 %F 317 %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2013a.pdf %0 Journal Article %A Pieper, U %A Chiang, R %A Seffernick, JJ %A Brown, SD %A Glasner, ME %A Kelly, L %A Eswar, N %A Sauder, JM %A Bonanno, JB %A Swaminathan, S %A Burley, SK %A Zheng, X %A Chance, MR %A Almo, SC %A Gerlt, JA %A Raushel, FM %A Jacobson, MP %A Babbitt, PC %A Sali, A %D 2009 %T Target selection and annotation for the structural genomics of the amidohydrolase and enolase superfamilies %B J Struct Funct Genom %V 10 %P 107-125 %7 2009/02/14 %8 Feb %! Target selection and annotation for the structural genomics of the amidohydrolase and enolase superfamilies. %@ 1345-711X %2 PMCID2693957 %M 19219566;PMCID:PMC2693957 %L 213 %F 213 %X To study the substrate specificity of enzymes, we use the amidohydrolase and enolase superfamilies as model systems; members of these superfamilies share a common TIM barrel fold and catalyze a wide range of chemical reactions. Here, we describe a collaboration between the Enzyme Specificity Consortium (ENSPEC) and the New York SGX Research Center for Structural Genomics (NYSGXRC) that aims to maximize the structural coverage of the amidohydrolase and enolase superfamilies. Using sequence- and structure-based protein comparisons, we first selected 535 target proteins from a variety of genomes for high-throughput structure determination by X-ray crystallography; 63 of these targets were not previously annotated as superfamily members. To date, 20 unique amidohydrolase and 41 unique enolase structures have been determined, increasing the fraction of sequences in the two superfamilies that can be modeled based on at least 30% sequence identity from 45% to 73%. We present case studies of proteins related to uronate isomerase (an amidohydrolase superfamily member) and mandelate racemase (an enolase superfamily member), to illustrate how this structure-focused approach can be used to generate hypotheses about sequence-structure-function relationships. %Z NIHMSID # 105380 %U http://salilab.org/pdf/Pieper_JStructFunctGenom_2009.pdf %+ Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biosciences, University of California at San Francisco, Byers Hall at Mission Bay, Office 501-32, 1700 4th Street, San Francisco, CA, 94158, USA, ursula@salilab.org. %G Eng %0 Journal Article %A Pieper, U %A Eswar, N %A Webb, BM %A Eramian, D %A Kelly, L %A Barkan, DT %A Carter, H %A Mankoo, P %A Karchin, R %A Marti-Renom, MA %A Davis, FP %A Sali, A %D 2009 %T MODBASE, a database of annotated comparative protein structure models and associated resources %B Nucleic Acids Res %V 37 %N Database issue %P D347-354 %8 Jan %! MODBASE, a database of annotated comparative protein structure models and associated resources. %@ 1362-4962 %2 PMCID2686492 %M 18948282;PMCID:PMC2686492 %L 214 %F 214 %X MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). %U http://salilab.org/pdf/Pieper_NucleicAcidsRes_2009.pdf %+ Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158, USA. %G eng %0 Journal Article %A Politis, A. %A Schmidt, C. %A Tjioe, E. %A Sandercock, A. %A Lasker, K. %A Gordiyenko, Y. %A Russel, D. %A Sali, A. %A Robinson, C. %D 2015 %T Topological models of heteromeric protein assemblies from mass spectrometry: Application to the yeast eIF3:eIF5 complex %B Chem Biol %V 22 %P 117-128 %! Topological models of heteromeric protein assemblies from mass spectrometry: Application to the yeast eIF3:eIF5 complex %2 PMCID4306531 %M 25544043;PMCID:PMC4306531 %L 333 %F 333 %U http://salilab.org/pdf/Politis_ChemBiol_2015.pdf %0 Journal Article %A Pourmal, S. %A Green, E. %A Bajaj, R. %A Chemmama, I. %A Knudsen, G.M. %A Gupta, M. %A Sali, A. %A Cheng, Y. %A Craik, C.S. %A Stroud, R.M. %A Kroetz, D.L. %D 2024 %T Structural Basis of Prostaglandin Efflux by MRP4 %B Nat Struct Mol Biol %V 31 %N 4 %P 621-632 %! Structural Basis of Prostaglandin Efflux by MRP4 %R 10.1038/s41594-023-01176-4 %M 38216659 %L 451 %F 451 %0 Journal Article %A Puizdar, V. %A Zajc, T. %A Zerovnik, E. %A Renko, M. %A Pieper, U. %A Eswar, N. %A Sali, A %A Dolenc, I. %A Turk, V. %D 2012 %T Biochemical Characterization and Structural Modeling of Human Cathepsin E Variant 2 in Comparison to the Wild-type Protein %B Biol Chem %V 393 %P 177-186 %! Biochemical Characterization and Structural Modeling of Human Cathepsin E Variant 2 in Comparison to the Wild-type Protein %2 PMCID4111641 %M 22718633;PMCID:PMC4111641 %L 278 %F 278 %U http://salilab.org/pdf/Puizdar_BiolChem_2012.pdf %0 Journal Article %A Qiao, Y. %A Wang, Z. %A Tan, F. %A Chen, J. %A Lin, J. %A Yang, J. %A Li, H. %A Wang, X. %A Sali, A. %A Zhang, L. %A Zhong, G. %D 2020 %T Enhancer Reprogramming within Pre-existing Topologically Associated Domains Promotes TGF-β-Induced EMT and Cancer Metastasis %B Mol Ther %V 28 %N 9 %P 2083-2095 %7 2020/06/12 %8 Jun 1 %! Enhancer Reprogramming within Pre-existing Topologically Associated Domains Promotes TGF-β-Induced EMT and Cancer Metastasis %@ 1525-0016 %R 10.1016/j.ymthe.2020.05.026 %2 PMCID7474343 %M 32526202 %L 405 %F 405 %K Emt Foxa2 Hi-C Tead2 Tead4 Tgfβ enhancer reprogramming epithelial-to-mesenchymal transition metastasis %X Transcription growth factor β (TGF-β) signaling-triggered epithelial-to-mesenchymal transition (EMT) process is associated with tumor stemness, metastasis, and chemotherapy resistance. However, the epigenomic basis for TGF-β-induced EMT remains largely unknown. Here we reveal that HDAC1-mediated global histone deacetylation and the gain of specific histone H3 lysine 27 acetylation (H3K27ac)-marked enhancers are essential for the TGF-β-induced EMT process. Enhancers gained upon TGF-β treatment are linked to gene activation of EMT markers and cancer metastasis. Notably, dynamic enhancer gain or loss mainly occurs within pre-existing topologically associated domains (TADs) in epithelial cells, with minimal three-dimensional (3D) genome architecture reorganization. Through motif enrichment analysis of enhancers that are lost or gained upon TGF-β stimulation, we identify FOXA2 as a key factor to activate epithelial-specific enhancer activity, and we also find that TEAD4 forms a complex with SMAD2/3 to mediate TGF-β signaling-triggered mesenchymal enhancer reprogramming. Together, our results implicate that key transcription-factor (TF)-mediated enhancer reprogramming modulates the developmental transition in TGF-β signaling-associated cancer metastasis. %Z 1525-0024 Qiao, Yunbo Wang, Zejian Tan, Fangzhi Chen, Jun Lin, Jianxiang Yang, Jie Li, Hui Wang, Xiongjun Sali, Andrej Zhang, Liye Zhong, Guisheng Journal Article United States Mol Ther. 2020 Jun 1:S1525-0016(20)30290-2. doi: 10.1016/j.ymthe.2020.05.026. %U https://salilab.org/pdf/Qiao_MolTher_2020.pdf %+ Precise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou 510006, China. Electronic address: ybqiao@gzhu.edu.cn. School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China University of Chinese Academy of Sciences, Beijing 100049, China. iHuman Institute, ShanghaiTech University, Shanghai, 201210, China. State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China. Precise Genome Engineering Center, School of Life Sciences, Guangzhou University, Guangzhou 510006, China. University of Chinese Academy of Sciences, Beijing 100049, China. Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA. School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China. Electronic address: zhangly@shanghaitech.edu.cn. School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China iHuman Institute, ShanghaiTech University, Shanghai, 201210, China. Electronic address: zhongsh@shanghaitech.edu.cn. %G eng %0 Journal Article %A Rafiei, A. %A Lee, L. %A Crowder, D.A. %A Saltzberg, D.J. %A Sali, A. %A Brouhard, G.J. %A Schriemer, D.C. %D 2022 %T Doublecortin engages the microtubule lattice through a cooperative binding mode involving its C-terminal domain %B eLife %V 11 %P e66975 %! Doublecortin engages the microtubule lattice through a cooperative binding mode involving its C-terminal domain %R 10.7554/eLife.66975 %2 PMCID9122500 %M 35485925 %L 433 %F 433 %U https://salilab.org/pdf/Rafiei_eLife_2022.pdf %0 Journal Article %A Rajashankar, K.R. %A Chance, M.R. %A Burley, S.K. %A Jiang, J. %A Almo, S.C. %A Bresnick, A.R. %A Dodatko, T. %A Huang, R. %A He, G. %A Chen, H. %A Sullivan, M. %A Toomey, J. %A Thirumuruhan, R.A. %A Franklin, W.A. %A Sali, A. %A Pieper, U. %A Eswar, N. %A Ilyin, V. %A McMahan, L. %D 2002 %T Structural Genomics at the National Synchrotron Light Source %B NSLS Activity Report 2001 %V 2 %P 28-32 %8 2002/// %! Structural Genomics at the National Synchrotron Light Source %L 112 %F 112 %K Genomics %Z TY - JOUR %U http://salilab.org/pdf/Rajashankar_NSLSActivityReport2001_2002.pdf %0 Journal Article %A Raveh, B. %A Eliasian, R. %A Rashkovits, S. %A Russel, D. %A Hayama, R. %A Sparks, S.E. %A Singh, D. %A Lim, R. %A Villa, E. %A Rout, M.P. %A Cowburn, D. %A Sali, A. %D 2024 %T Integrative spatiotemporal map of nucleocytoplasmic transport %B in press %! Integrative spatiotemporal map of nucleocytoplasmic transport %R 10.1101/2023.12.31.573409 %2 PMCID10802240 %M 38260487 %L 453 %F 453 %0 Journal Article %A Raveh, B. %A Karp, J. M. %A Sparks, S. %A Dutta, K. %A Rout, M. P. %A Sali, A. %A Cowburn, D. %D 2016 %T Slide-and-exchange mechanism for rapid and selective transport through the nuclear pore complex %B Proc Natl Acad Sci USA %V 113 %N 18 %P E2489-97 %! Slide-and-exchange mechanism for rapid and selective transport through the nuclear pore complex %R 10.1073/pnas.1522663113 %2 PMCID4983827 %M 27091992 %L 351 %F 351 %U http://salilab.org/pdf/Raveh_ProcNatlAcadSciUSA_2016.pdf %0 Journal Article %A Raveh, B. %A Sun, L. %A White, K.L. %A Sanyal, T. %A Tempkin, J. %A Zheng, D. %A Pilla, K.B. %A Singla, J. %A Wang, C. %A Zha, J. %A Li, A. %A Graham, N.A. %A Kesselman, C. %A Stevens, R.C. %A Sali, A. %D 2021 %T Bayesian metamodeling of complex biological systems across varying representations %B Proc Natl Acad Sci USA %V 118 %6 35 %P e2104559118 %! Bayesian metamodeling of complex biological systems across varying representations %R 10.1073/pnas.2104559118 %2 PMCID8536362 %M 34453000 %L 415 %F 415 %U https://salilab.org/pdf/Raveh_ProcNatlAcadSciUSA_2021.pdf %0 Journal Article %A Reid, R. %A Sgobba, M. %A Raveh, B. %A Rastelli, G. %A Sali, A. %A Santi, D. %D 2015 %T Analytical and simulation-based models for drug release and gel-degradation in a Tetra-PEG hydrogel drug-delivery system %B Macromolecules %V 48 %N 19 %P 7359-7369 %! Analytical and simulation-based models for drug release and gel-degradation in a Tetra-PEG hydrogel drug-delivery system %R 10.1021/acs.macromol.5b01598 %L 341 %F 341 %U https://salilab.org/pdf/Reid_Macromolecules_2015.pdf %0 Journal Article %A Renko, M. %A Sali, A. %A Turk, V. %A Pokomy, M. %A Kregar, I. %D 1985 %T A neutral metalloproteinase from Streptomyces rimosus %B Vestnik Slovenskega Kemijskega Drustva %V 32/2 %P 161-173 %8 1985/03/19/ %! A neutral metalloproteinase from Streptomyces rimosus %L 1 %F 1 %Z TY - JOUR %U http://salilab.org/pdf/Renko_VestnikSlovenskegaKemijskegaDrustva_1985.pdf %0 Journal Article %A Rettenmaier, T. %A Fan, H. %A Karpiak, J. %A Doak, A. %A Sali, A. %A Shoichet, B. %A Wells, J. %D 2015 %T Small-molecule allosteric modulators of the protein kinase PDK1 from structure-based docking %B J Med Chem %V 58 %N 20 %P 8285-91 %7 2015 Oct 12 %! Small-molecule allosteric modulators of the protein kinase PDK1 from structure-based docking %2 PMCID5099076 %$ NIHMS826510 %M 26443011 %L 344 %F 344 %U http://salilab.org/pdf/Rettenmaier_JMedChem_2015.pdf %0 Journal Article %A Robinson, CV %A Sali, A %A Baumeister, W %D 2007 %T The molecular sociology of the cell %B Nature %V 450 %N 7172 %P 973-982 %8 Dec %! The molecular sociology of the cell. %@ 1476-4687 %M 18075576 %L 189 %F 189 %K Cells Cryoelectron Microscopy Humans Mass Spectrometry Proteasome Endopeptidase Complex Proteomics Ribosomes %X Proteomic studies have yielded detailed lists of the proteins present in a cell. Comparatively little is known, however, about how these proteins interact and are spatially arranged within the 'functional modules' of the cell: that is, the 'molecular sociology' of the cell. This gap is now being bridged by using emerging experimental techniques, such as mass spectrometry of complexes and single-particle cryo-electron microscopy, to complement traditional biochemical and biophysical methods. With the development of integrative computational methods to exploit the data obtained, such hybrid approaches will uncover the molecular architectures, and perhaps even atomic models, of many protein complexes. With these structures in hand, researchers will be poised to use cryo-electron tomography to view protein complexes in action within cells, providing unprecedented insights into protein-interaction networks. %U http://salilab.org/pdf/Robinson_Nature_2007.pdf %+ Department of Chemistry, Lensfield Road, University of Cambridge, Cambridge CB2 1EW, UK. cvr24@cam.ac.uk %G eng %0 Journal Article %A Robinson, P. %A Trnka, M. %A Pellarin, R. %A Greenberg, C. %A Bushnell, D. %A Davis, R. %A Burlingame, A. %A Sali, A. %A Kornberg, R. %D 2015 %T Molecular architecture of the yeast Mediator complex %B eLife %V 4 %P e08719 %! Molecular architecture of the yeast Mediator complex %R 10.7554/eLife.08719 %2 PMCID4631838 %M 26402457 %L 340 %F 340 %U https://salilab.org/pdf/Robinson_eLife_2015.pdf %0 Journal Article %A Rossi, A. %A Deveraux, Q. %A Turk, B. %A Sali, A. %D 2004 %T Comprehensive search for cysteine cathepsins in the human genome %B Biol Chem %V 385 %N 5 %P 363-372 %8 May %! Comprehensive search for cysteine cathepsins in the human genome %M 15195995 %L 130 %F 130 %K Cathepsins/*genetics/metabolism Cysteine/analysis/metabolism Cysteine Endopeptidases/*genetics/metabolism Databases, Protein *Genome, Human Human Genome Project Humans RNA, Messenger/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Alignment Sequence Homology %X Our study was aimed at examinating whether or not the human genome encodes for previously unreported cysteine cathepsins. To this end, we used analyses of the genome sequence and mRNA expression levels. The program TBLASTN was employed to scan the draft sequence of the human genome for the 11 known cysteine cathepsins. The cathepsin-like segments in the genome were inspected, filtered, and annotated. In addition to the known cysteine cathepsins, the scan identified three pseudogenes, closely related to cathepsin L, on chromosome 10, as well as two remote homologs, tubulointerstitial protein antigen and tubulointerstitial protein antigen-related protein. No new members of the family were identified. mRNA expression profiles for 10 known human cysteine cathepsins showed varying expression levels in 46 different human tissues and cell lines. No expression of any of the three cathepsin L-like pseudogenes was found. Based on these results, it is likely that to date all human cysteine cathepsins are known. %Z 1431-6730 Journal Article %U http://salilab.org/pdf/Rossi_BiolChem_2004.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, University of California at San Francisco, San Francisco, CA 94143-2240, USA. %0 Journal Article %A Rossi, A. %A Marti-Renom, M. A. %A Sali, A. %D 2006 %T Localization of binding sites in protein structures by optimization of a composite scoring function %B Protein Sci %V 15 %N 10 %P 2366-2380 %8 Oct %! Localization of binding sites in protein structures by optimization of a composite scoring function %M 16963645 %L 166 %F 166 %K Amino Acid Sequence Binding Sites Biomechanics Electrostatics Hydrophobicity Ligands *Monte Carlo Method Protein Conformation Proteins/*chemistry %X The rise in the number of functionally uncharacterized protein structures is increasing the demand for structure-based methods for functional annotation. Here, we describe a method for predicting the location of a binding site of a given type on a target protein structure. The method begins by constructing a scoring function, followed by a Monte Carlo optimization, to find a good scoring patch on the protein surface. The scoring function is a weighted linear combination of the z-scores of various properties of protein structure and sequence, including amino acid residue conservation, compactness, protrusion, convexity, rigidity, hydrophobicity, and charge density; the weights are calculated from a set of previously identified instances of the binding-site type on known protein structures. The scoring function can easily incorporate different types of information useful in localization, thus increasing the applicability and accuracy of the approach. To test the method, 1008 known protein structures were split into 20 different groups according to the type of the bound ligand. For nonsugar ligands, such as various nucleotides, binding sites were correctly identified in 55%-73% of the cases. The method is completely automated (http://salilab.org/patcher) and can be applied on a large scale in a structural genomics setting. %Z 0961-8368 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Rossi_ProteinSci_2006.pdf %+ Department of Biopharmaceutical Sciences and Pharmaceutical Chemistry, California Institute for Quantitative Biomedical Research, University of California, San Francisco, California 94143-2552, USA. andrea@salilab.org %0 Journal Article %A Rout, MP %A Sali, A %D 2019 %T Principles for Integrative Structural Biology Studies %B Cell %V 177 %P 1384-1403 %! Principles for Integrative Structural Biology Studies %R 10.1016/j.cell.2019.05.016 %2 PMCID6810593 %M 31150619 %L 395 %F 395 %U https://salilab.org/pdf/Rout_Cell_2019.pdf %0 Journal Article %A Russel, D. %A Lasker, K. %A Webb, B. %A Velazquez-Muriel, J. %A Tjioe, E. %A Schneidman-Duhovny, D. %A Peterson, B. %A Sali, A. %D 2012 %T Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies %B PLoS Biol %V 10 %N 1 %P e1001244 %! Putting the pieces together: integrative modeling platform software for structure determination of macromolecular assemblies %2 PMCID3260315 %M 22272186;PMCID:PMC3260315 %L 272 %F 272 %U http://salilab.org/pdf/Russel_PLoSBiol_2012.pdf %0 Journal Article %A Russel, D %A Lasker, K %A Phillips, J %A Schneidman-Duhovny, D %A Velazquez-Muriel, JA %A Sali, A %D 2009 %T The structural dynamics of macromolecular processes %B Curr Opin Cell Biol %V 21 %P 97-108 %8 Feb %! The structural dynamics of macromolecular processes. %@ 1879-0410 %2 PMCID2774249 %M 19223165;PMCID:PMC Journal- In Progress %L 218 %F 218 %X Dynamic processes involving macromolecular complexes are essential to cell function. These processes take place over a wide variety of length scales from nanometers to micrometers, and over time scales from nanoseconds to minutes. As a result, information from a variety of different experimental and computational approaches is required. We review the relevant sources of information and introduce a framework for integrating the data to produce representations of dynamic processes. %U http://salilab.org/pdf/Russel_CurrOpinCellBiol_2009.pdf %+ Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical, Chemistry, and California Institute for Quantitative Biosciences, Byers Hall, Suite 503B, University of California at San Francisco, 1700 4th Street, San Francisco, CA 94158-2330, USA. %G Eng %0 Journal Article %A Russel, M. %A Linderoth, N. A. %A Sali, A. %D 1997 %T Filamentous phage assembly: variation on a protein export theme %B Gene %V 192 %N 1 %P 23-32 %8 Jun 11 %! Filamentous phage assembly: variation on a protein export theme %M 9224870 %L 52 %F 52 %K Bacterial Outer Membrane Proteins/genetics/metabolism Bacterial Proteins/genetics/metabolism Bacteriophages/genetics/*metabolism Biological Transport/physiology Fimbriae Proteins Fimbriae, Bacterial/genetics/*metabolism Genes, Viral Protein Conformation Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Alignment Viral Proteins/*metabolism %X Biogenesis of both filamentous phage and type-IV pili involves the assembly of many copies of a small, integral inner membrane protein (the phage major coat protein or pilin) into a helical, tubular array that passes through the outer membrane. The occurrence of related proteins required for assembly and export in both systems suggests that there may be similarities at the mechanistic level as well. This report summarizes the properties of filamentous phage and the proteins required for their assembly, with particular emphasis on features they may share with bacterial protein export and pilus biogenesis systems, and it presents evidence that supports the hypothesis that one of the phage proteins functions as an outer membrane export channel. %Z 0378-1119 Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Russel_Gene_1997.pdf %+ The Rockefeller University, New York, NY 10021, USA. russelm@rockvax.rockefeller.edu %0 Journal Article %A Russell, R. B. %A Alber, F. %A Aloy, P. %A Davis, F. P. %A Korkin, D. %A Pichaud, M. %A Topf, M. %A Sali, A. %D 2004 %T A structural perspective on protein-protein interactions %B Curr Opin Struct Biol %V 14 %N 3 %P 313-324 %8 Jun %! A structural perspective on protein-protein interactions %M 15193311 %L 139 %F 139 %K Animals Crystallography, X-Ray/methods Humans Magnetic Resonance Spectroscopy Microscopy, Electron/methods Models, Molecular Protein Conformation *Protein Interaction Mapping Proteins/*chemistry/*metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Structural Homology, Protein Tomography/methods %X Structures of macromolecular complexes are necessary for a mechanistic description of biochemical and cellular processes. They can be solved by experimental methods, such as X-ray crystallography, NMR spectroscopy and electron microscopy, as well as by computational protein structure prediction, docking and bioinformatics. Recent advances and applications of these methods emphasize the need for hybrid approaches that combine a variety of data to achieve better efficiency, accuracy, resolution and completeness. %Z 0959-440x Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Russell_CurrOpinStructBiol_2004.pdf %+ EMBL, Heidelberg, Germany. %0 Journal Article %A Ryan, C. %A Cimermancic, P. %A Szpiechy, Z. %A Sali, A. %A Hernandez, R. %A Krogan, N. %D 2013 %T High-resolution network biology: connecting sequence with function %B Nat Rev Genet %V 14 %P 865-879 %! High-resolution network biology: connecting sequence with function %2 PMCID4023809 %M 24197012;PMCID:PMC4023809 %L 312 %F 312 %U http://salilab.org/pdf/Ryan_NatRevGenet_2013.pdf %0 Journal Article %A Salcedo, E.C. %A Winter, M.B. %A Khuri, N. %A Knudsen, G.M. %A Sali, A. %A Craik, C.S. %D 2021 %T Global Protease Activity Profiling Identifies HER2-Driven Proteolysis in Breast Cancer %B ACS Chem. Biol. %V 16 %6 4 %P 712-723 %! Global Protease Activity Profiling Identifies HER2-Driven Proteolysis in Breast Cancer %R 10.1021/acschembio.0c01000 %M 33765766 %L 413 %F 413 %U https://salilab.org/pdf/Salcedo_ACSChemBiol_2021.pdf %0 Journal Article %A Sali, A %D 2003 %T NIH workshop on structural proteomics of biological complexes %B Structure %V 11 %N 9 %P 1043-1047 %8 Sep %! NIH workshop on structural proteomics of biological complexes. %@ 0969-2126 %M 12962622 %L 127 %F 127 %K Macromolecular Substances Molecular Structure Proteomics %X Recently, some 50 biologists and officials from government funding agencies met at the NIH campus in Bethesda, MD to explore the interdisciplinary science and organization of the emerging field of structural proteomics. Structural proteomics aims to discover most macromolecular complexes and characterize their three-dimensional structures and functional mechanisms in space and time. The goal seems daunting, but the consensus was that the prize would be commensurate with the effort invested, given the importance of molecular machines and functional networks in biology and medicine. Identification of assemblies and transient complexes combined with their structural and functional characterization will allow us to understand, control, design, and change the functioning of larger biological systems as well as to contribute to drug target discovery, lead discovery, and lead optimization for treatment of human disease. %U http://salilab.org/pdf/Sali_Structure_2003.pdf %+ Department of Biopharmaceutical Sciences and California Institute for Quantitative Biomedical Research, University of California, San Francisco, San Francisco, CA 94143, USA. sali@salilab.org %G eng %0 Journal Article %A Sali, A. %D 1995 %T Comparative protein modeling by satisfaction of spatial restraints %B Mol Med Today %V 1 %N 6 %P 270-277 %8 Sep %! Comparative protein modeling by satisfaction of spatial restraints %M 9415161 %L 41 %F 41 %K Amino Acid Sequence Animals Comparative Study Computer Simulation Crystallography, X-Ray Mice *Models, Molecular Molecular Sequence Data Nuclear Magnetic Resonance, Biomolecular *Protein Conformation Proteins/*chemistry Serine Endopeptidases/chemistry Software %X Approximately one third of known protein sequences are related to at least one known protein structure. As a result, an order of magnitude more sequences can be modeled by comparative modeling than there are experimentally determined protein structures. A large fraction of these models has an accuracy approaching that of a low resolution X-ray structure or a medium resolution nuclear magnetic resonance structure. The number of applications where homology modeling has been proven useful is growing rapidly. %Z 1357-4310 Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Sali_MolMedToday_1995.pdf %+ Rockefeller University, New York, NY 10021-6399, USA. sali@rockvax.rockefeller.edu %0 Journal Article %A Sali, A. %D 1995 %T Modeling mutations and homologous proteins %B Curr Opin Biotechnol %V 6 %N 4 %P 437-451 %8 Aug %! Modeling mutations and homologous proteins %M 7579655 %L 45 %F 45 %K Amino Acid Sequence *Computer Simulation Databases, Factual *Models, Molecular *Mutation *Protein Conformation Proteins/*chemistry Sequence Alignment %X A protein sequence with at lease 40% identity to a known structure can now be modelled automatically, with an accuracy approaching that o fa low-resolution X-ray structure or a medium-resolution nuclear magnetic resonance structure. In general, these models have goods stereochemistry and an overall structural accuracy that is as high as the similarity between the template and the actual structure being predicted. As a result, the number of sequences that can be modelled is an order of magnitude larger then the number of experimentally determined protein structures. In addition, evaluation techniques are available that can estimated errors in different regions of the model. Thus, the number of applications where homology modelling is proving useful is growing rapidly. %Z 0958-1669 Journal Article Review %U http://salilab.org/pdf/Sali_CurrOpinBiotechnol_1995.pdf %+ The Rockefeller University, New York, USA. %0 Book Section %A Sali, A. %D 1995 %T MODELLER: Implementing 3D protein modeling %B mc^2 %I Molecular Simulations Inc. %V 2 %P 5 %! MODELLER: Implementing 3D protein modeling %L 38 %F 38 %Z TY - CHAP %U http://salilab.org/pdf/Sali_mc2_1995.pdf %+ Burlington, US %0 Journal Article %A Sali, A. %D 1998 %T 100,000 protein structures for the biologist %B Nat Struct Biol %V 5 %N 12 %P 1029-1032 %8 Dec %! 100,000 protein structures for the biologist %M 9846869 %L 68 %F 68 %K Animals Cloning, Molecular Crystallography, X-Ray Databases, Factual Humans Internet Magnetic Resonance Spectroscopy *Molecular Biology/economics Peptide Library *Protein Conformation Proteins/genetics %X Structural genomics promises to deliver experimentally determined three-dimensional structures for many thousands of protein domains. These domains will be carefully selected, so that the methods of fold assignment and comparative protein structure modeling will result in useful models for most other protein sequences. The impact on biology will be dramatic. %Z 1072-8368 Congresses Journal Article Review Review, Tutorial %U http://salilab.org/pdf/Sali_NatStructBiol_1998.pdf %+ Laboratories of Molecular Biophysics, The Rockefeller University, New York, New York 10021, USA. sali@rockefeller.edu %0 Journal Article %A Sali, A. %D 1999 %T Functional links between proteins %B Nature %V 402 %N 6757 %P 23, 25-26 %8 Nov 4 %! Functional links between proteins %M 10573411 %L 77 %F 77 %K Computational Biology Phylogeny Protein Binding Protein Conformation Proteins/chemistry/classification/genetics/*physiology Proteome RNA, Messenger/biosynthesis %Z 0028-0836 Comment News %U http://salilab.org/pdf/Sali_Nature_1999.pdf %0 Journal Article %A Sali, A. %D 2001 %T Target practice %B Nat Struct Biol %V 8 %N 6 %P 482-484 %8 Jun %! Target practice %M 11373610 %L 96 %F 96 %K Animals *Computational Biology Genomics Humans *Models, Molecular Protein Structure, Tertiary Proteome/*chemistry Research Design Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid %Z 1072-8368 Comment News %U http://salilab.org/pdf/Sali_NatStructBiol_2001.pdf %0 Journal Article %A Sali, A. %D 2021 %T From integrative structural biology to cell biology %B J Biol Chem %P 100743 %7 2021/05/03 %8 May %! From integrative structural biology to cell biology %@ 1083-351X %R 10.1016/j.jbc.2021.100743 %2 PMCID8203844 %M 33957123 %L 421 %F 421 %K cell biology integrative modeling integrative structural biology integrative structure modeling structural biology %X Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52 MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell. %U https://salilab.org/pdf/Sali_JBiolChem_2021.pdf %G eng %0 Journal Article %A Sali, A. %A Berman, H. %A Schwede, T. %A Trewhella, J. %A Kleywegt, G. %A Burley, S. %A Markley, J. %A Nakamura, H. %A Adams, P. %A Bonvin, A. %A Chiu, W. %A Dal Peraro, M. %A Di Maio, F. %A Ferrin, T. %A Grunewald, K. %A Gutmanas, A. %A Henderson, R. %A Hummer, G. %A Iwasaki, K. %A Johnson, G. %A Lawson, C. %A Meiler, J. %A Marti-Renom, M. %A Montelione, G. %A Nilges, M. %A Nussinov, R. %A Patwardhan, A. %A Rappsilber, J. %A Read, R. %A Saibil, H. %A Schroder, G. %A Schwieters, C. %A Seidel, C. %A Svergun, D. %A Topf, M. %A Ulrich, E. %A Velanker, S. %A Westbrook, J. %D 2015 %T Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop %B Structure %V 23 %N 7 %P 1156-67 %& 1156 %7 1167 %! Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop %R 10.1016/j.str.2015.05.013 %2 PMCID4933300 %M 26095030 %L 336 %F 336 %U http://salilab.org/pdf/Sali_Structure_2015.pdf %0 Book Section %A Sali, A. %A Blundell, T. %D 1994 %T Comparative protein modeling by statisfaction of spatial restraints %E Bohr, H. %E Brunak, S. %B Protein Structure by Distance Analysis %C LYNGBY, DENMARK %I TECH UNIV DENMARK, CTR BIOL SEQUENCE ANAL %P 64-86 %S Symposium on Distance Based Approaches to Protein Structure Determination %! Comparative protein modeling by statisfaction of spatial restraints %L 31 %F 31 %Z Bohr, H Brunak, S Symposium on Distance Based Approaches to Protein Structure Determination NOV 23-26, 1993 TECH UNIV DENMARK, CTR BIOL SEQUENCE ANAL, LYNGBY, DENMARK %U http://salilab.org/pdf/Sali_ProtStrucDistAna_1994.pdf %0 Journal Article %A Sali, A. %A Blundell, T. L. %D 1990 %T Definition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships through simulated annealing and dynamic programming %B J Mol Biol %V 212 %N 2 %P 403-428 %8 Mar 20 %! Definition of general topological equivalence in protein structures. A procedure involving comparison of properties and relationships through simulated annealing and dynamic programming %M 2181150 %L 11 %F 11 %K Amino Acid Sequence Aspartic Endopeptidases *Endopeptidases *Globins Methods Molecular Sequence Data Protein Conformation Research Support, Non-U.S. Gov't Sequence Homology, Nucleic Acid *Serine Endopeptidases %X A protein is defined as an indexed string of elements at each level in the hierarchy of protein structure: sequence, secondary structure, super-secondary structure, etc. The elements, for example, residues or secondary structure segments such as helices or beta-strands, are associated with a series of properties and can be involved in a number of relationships with other elements. Element-by-element dissimilarity matrices are then computed and used in the alignment procedure based on the sequence alignment algorithm of Needleman & Wunsch, expanded by the simulated annealing technique to take into account relationships as well as properties. The utility of this method for exploring the variability of various aspects of protein structure and for comparing distantly related proteins is demonstrated by multiple alignment of serine proteinases, aspartic proteinase lobes and globins. %Z 0022-2836 Journal Article %U http://salilab.org/pdf/Sali_JMolBiol_1990.pdf %+ Department of Crystallography, Birkbeck College, University of London, England. %0 Journal Article %A Sali, A. %A Blundell, T. L. %D 1993 %T Comparative protein modelling by satisfaction of spatial restraints %B J Mol Biol %V 234 %N 3 %P 779-815 %8 Dec 5 %! Comparative protein modelling by satisfaction of spatial restraints %M 8254673 %L 29 %F 29 %K Amino Acid Sequence Animals Comparative Study Enzymes/chemistry Humans Kallikreins/chemistry *Mathematics *Models, Theoretical Molecular Sequence Data Pancreatic Elastase/chemistry Probability *Protein Conformation Proteins/*chemistry Research Support, Non-U.S. Gov't Sequence Homology, Amino Acid Software Thermodynamics Tissue Kallikreins Trypsin/chemistry %X We describe a comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures. The three-dimensional (3D) model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions (pdfs) for the features restrained. For example, the probabilities for main-chain conformations of a modelled residue may be restrained by its residue type, main-chain conformation of an equivalent residue in a related protein, and the local similarity between the two sequences. Several such pdfs are obtained from the correlations between structural features in 17 families of homologous proteins which have been aligned on the basis of their 3D structures. The pdfs restrain C alpha-C alpha distances, main-chain N-O distances, main-chain and side-chain dihedral angles. A smoothing procedure is used in the derivation of these relationships to minimize the problem of a sparse database. The 3D model of a protein is obtained by optimization of the molecular pdf such that the model violates the input restraints as little as possible. The molecular pdf is derived as a combination of pdfs restraining individual spatial features of the whole molecule. The optimization procedure is a variable target function method that applies the conjugate gradients algorithm to positions of all non-hydrogen atoms. The method is automated and is illustrated by the modelling of trypsin from two other serine proteinases. %Z 0022-2836 Journal Article %U http://salilab.org/pdf/Sali_JMolBiol_1993.pdf %+ Department of Crystallography, Birkbeck College, London, England. %0 Journal Article %A Sali, A. %A Chiu, W. %D 2005 %T Macromolecular assemblies highlighted %B Structure %V 13 %N 3 %P 339-341 %8 Mar %! Macromolecular assemblies highlighted %L 151 %F 151 %X The functional units in cells are often assemblies of macromolecules, including proteins and nucleic acids. Therefore, the knowledge of structure, dynamics, and energetics of macromolecular complexes is necessary for a mechanistic description of biochemical and cellular processes. This special issue of Structure, "Macromolecular Assemblies Highlighted," presents ten research articles and five reviews on the determination, representation, visualization, archival, and dissemination of assembly structure and dynamics. %Z 0969-2126 Editorial %U http://salilab.org/pdf/Sali_Structure_2005.pdf %0 Journal Article %A Sali, A. %A Glaeser, R. %A Earnest, T. %A Baumeister, W. %D 2003 %T From words to literature in structural proteomics %B Nature %V 422 %N 6928 %P 216-225 %! From words to literature in structural proteomics %@ 0028-0836 %R 10.1038/nature01513 %M 12634795 %L 120 %F 120 %U http://salilab.org/pdf/Sali_Nature_2003.pdf %0 Journal Article %A Sali, A. %A Kuriyan, J. %D 1999 %T Challenges at the frontiers of structural biology (Reprinted from Trends in Biochemical Science, vol 12, Dec., 1999) %B Trends Cell Biol %V 9 %P M20-M24 %! Challenges at the frontiers of structural biology (Reprinted from Trends in Biochemical Science, vol 12, Dec., 1999) %@ 0962-8924 %M 10611675 %L 76 %F 76 %X Knowledge of the three-dimensional structures of proteins is the key to unlocking the full potential of genomic information. There are two distinct directions along which cutting-edge research in structural biology is currently moving towards this goal. On the one hand, tightly focused Long-term research in individual laboratories is leading to the determination of the structures of macromolecular assemblies of ever-increasing size and complexity. On the other hand, Large consortia of structural biologists, inspired by the pace of genome sequencing, are developing strategies to determine new protein structures rapidly, so that it will soon be possible to predict reasonably accurate structures for most protein domains. We anticipate that a small number of complex systems, studied in depth, will provide insights across the field of biology with the aid of genome-based comparative structural analysis. %U http://salilab.org/pdf/Sali_TrendsCellBiol_1999.pdf %0 Journal Article %A Sali, A. %A Marti-Renom, M.A. %A Madhusudhan, M.S. %A Fiser, A. %A Rost, B. %D 2002 %T Reply to Moult et al %B Structure %V 10 %P 292-293 %8 2002/// %! Reply to Moult et al. %L 108 %F 108 %Z TY - JOUR %U http://salilab.org/pdf/Sali_Structure_2002.pdf %0 Journal Article %A Sali, A. %A Matsumoto, R. %A McNeil, H. P. %A Karplus, M. %A Stevens, R. L. %D 1993 %T Three-dimensional models of four mouse mast cell chymases. Identification of proteoglycan-binding regions and protease-specific antigenic epitopes %B J Biol Chem %V 268 %N 12 %P 9023-9034 %! Three-dimensional models of four mouse mast cell chymases. Identification of proteoglycan-binding regions and protease-specific antigenic epitopes %@ 0021-9258 %M 7682557 %L 30 %F 30 %X Mouse mast cell protease (mMCP) 1, mMCP-2, MMCP-4, and mMCP-5 are serine proteases which are predicted to have chymotryptic specificity (chymases). They are bound to negatively charged heparin or chondroitin sulfate proteoglycans and are stored in secretory granules. Three-dimensional (3D) models of these four proteases were constructed with a comparative molecular modeling technique based on satisfaction of spatial constraints. The models were used to predict immunogenic epitopes and surface regions that are likely to interact with proteoglycans. Nine potential antigenic segments in the four chymases were identified on the basis of solvent accessibility, protrusion, flexibility, and sequence variability. These segments are suitable epitopes for preparation of protease-specific antipeptide immunoglobulin. Two regions with net charges ranging from +6 to +10 at neutral pH were found on the surfaces of mMCP-4 and mMCP-5. The two regions are located far from the substrate binding cleft at diametrically opposite ends of the folded proteases. A strong positive electrostatic potential surrounds the two regions. Thus, they are good candidates for binding sites that interact with heparin proteoglycan in the granules of serosal mast cells. In contrast, mMCP-1 and mMCP-2, which are present in granules of mucosal mast cells that contain chondroitin sulfate, lack one of these regions and have a lower charge density in the other. The differences between the 3D models provide a structural basis for the selective localization of specific chymases within mouse mast cells that contain different proteoglycans. %U http://salilab.org/pdf/Sali_JBiolChem_1993.pdf %0 Journal Article %A Sali, A. %A Overington, J. P. %D 1994 %T Derivation of rules for comparative protein modeling from a database of protein structure alignments %B Protein Sci %V 3 %N 9 %P 1582-1596 %8 Sep %! Derivation of rules for comparative protein modeling from a database of protein structure alignments %M 7833817 %L 32 %F 32 %K Amino Acid Sequence Comparative Study *Databases, Factual Disulfides/chemistry Isomerism Least-Squares Analysis *Models, Chemical Molecular Sequence Data Proline/chemistry *Protein Conformation Research Support, Non-U.S. Gov't Sequence Alignment/*methods %X We describe a database of protein structure alignments as well as methods and tools that use this database to improve comparative protein modeling. The current version of the database contains 105 alignments of similar proteins or protein segments. The database comprises 416 entries, 78,495 residues, 1,233 equivalent entry pairs, and 230,396 pairs of equivalent alignment positions. At present, the main application of the database is to improve comparative modeling by satisfaction of spatial restraints implemented in the program MODELLER (Sali A, Blundell TL, 1993, J Mol Biol 234:779-815). To illustrate the usefulness of the database, the restraints on the conformation of a disulfide bridge provided by an equivalent disulfide bridge in a related structure are derived from the alignments; the prediction success of the disulfide dihedral angle classes is increased to approximately 80%, compared to approximately 55% for modeling that relies on the stereochemistry of disulfide bridges alone. The second example of the use of the database is the derivation of the probability density function for comparative modeling of the cis/trans isomerism of the proline residues; the prediction success is increased from 0% to 82.9% for cis-proline and from 93.3% to 96.2% for trans-proline. The database is available via electronic mail. %Z 0961-8368 Journal Article %U http://salilab.org/pdf/Sali_ProteinSci_1994.pdf %+ Department of Chemistry, Harvard University, Cambridge, Massachusetts 02138. %0 Journal Article %A Sali, A. %A Overington, J. P. %A Johnson, M. S. %A Blundell, T. L. %D 1990 %T From Comparisons of protein sequences and structures to protein modelling and design %B Trends Biochem Sci %V 15 %N 6 %P 235-240 %! From Comparisons of protein sequences and structures to protein modelling and design %M 2200167 %L 12 %F 12 %U http://salilab.org/pdf/Sali_TrendsBiochemSci_1990.pdf %0 Book Section %A Sali, A. %A Overington, J.P. %A Johnson, M.S. %A Blundell, T.L. %D 1991 %T From modelling homologous proteins to prediction of structure %E Goodfellow, J. M. %E Moss, D. S. %B Protein design and the development of new therapeutics and vaccines %I Ellis Horwood Ltd. %P 231-245 %! From modelling homologous proteins to prediction of structure %L 22 %F 22 %K Proteins %Z TY - CHAP %U http://salilab.org/pdf/Sali_ProtDesDevTheraVacc_1991.pdf %+ New York, US %0 Journal Article %A Sali, A. %A Potterton, L. %A Yuan, F. %A van Vlijmen, H. %A Karplus, M. %D 1995 %T Evaluation of comparative protein modeling by MODELLER %B Proteins %V 23 %N 3 %P 318-326 %8 Nov %! Evaluation of comparative protein modeling by MODELLER %M 8710825 %L 44 %F 44 %K Amino Acid Sequence Animals Computer Communication Networks Computer Graphics Computer Simulation Crystallography, X-Ray Databases, Factual Eosinophil-Derived Neurotoxin Humans Magnetic Resonance Spectroscopy Mice *Models, Molecular Molecular Sequence Data Neurotoxins/*chemistry Nucleoside-Diphosphate Kinase/*chemistry *Protein Conformation Protein Structure, Tertiary Receptors, Retinoic Acid/*chemistry Research Support, U.S. Gov't, Non-P.H.S. *Ribonucleases Sequence Alignment *Software Templates, Genetic %X We evaluate 3D models of human nucleoside diphosphate kinase, mouse cellular retinoic acid binding protein I, and human eosinophil neurotoxin that were calculated by MODELLER, a program for comparative protein modeling by satisfaction of spatial restraints. The models have good stereochemistry and are at least as similar to the crystallographic structures as the closest template structures. The largest errors occur in the regions that were not aligned correctly or where the template structures are not similar to the correct structure. These regions correspond predominantly to exposed loops, insertions of any length, and non-conserved side chains. When a template structure with more than 40% sequence identity to the target protein is available, the model is likely to have about 90% of the mainchain atoms modeled with an rms deviation from the X-ray structure of approximately 1 A, in large part because the templates are likely to be that similar to the X-ray structure of the target. This rms deviation is comparable to the overall differences between refined NMR and X-ray crystallography structures of the same protein. %Z 0887-3585 Journal Article %U http://salilab.org/pdf/Sali_Proteins_1995.pdf %+ Rockefeller University, New York, NY 10021, USA. %0 Journal Article %A Sali, A. %A Shakhnovich, E. %A Karplus, M. %D 1994 %T Kinetics of protein folding. A lattice model study of the requirements for folding to the native state %B J Mol Biol %V 235 %N 5 %P 1614-1636 %8 Feb 4 %! Kinetics of protein folding. A lattice model study of the requirements for folding to the native state %M 8107095 %L 33 %F 33 %K Algorithms *Amino Acid Sequence Comparative Study Kinetics Mathematics *Models, Theoretical Monte Carlo Method *Protein Conformation *Protein Folding Protein Structure, Secondary Proteins/*chemistry/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Thermodynamics %X A three-dimensional lattice model of a protein is used to investigate the properties required for its folding to the native state. The polypeptide chain is represented as a 27 bead heteropolymer whose lowest energy (native) state can be determined by an exhaustive enumeration of all fully compact conformations. A total of 200 sequences with random interactions are generated and subjected to Monte Carlo simulations to determine which chains find the ground state in a short time; i.e. which sequences overcome the folding problem referred to as the Levinthal paradox. Comparison of the folding and non-folding sequences is used to identify the features that are required for fast folding to the global energy minimum. It is shown that successful folding does not require certain attributes that have been previously proposed as necessary for folding; these include a high number of short versus long-range contacts in the native state, a high content of the secondary structure in the native state, a strong correlation between the native contact map and the interaction parameters, and the existence of a high number of low energy states with near-native conformation. Instead, the essential difference between the folding and the non-folding sequences is the nature of the energy spectrum. The necessary and sufficient condition for a sequence to fold rapidly in the present model is that the native state is a pronounced energy minimum. As a consequence, the thermodynamic stability of the native state of a folding sequence has a sigmoidal dependence on temperature. This permits such a sequence to satisfy both the thermodynamic and the kinetic requirements for folding; i.e. the native state predominates thermodynamically at temperatures that are high enough for folding to be kinetically possible. The applicability of the present results to real proteins is discussed. %Z 0022-2836 Journal Article %U http://salilab.org/pdf/Sali_JMolBiol_1994.pdf %+ Department of Chemistry, Harvard University, Cambridge, MA 02138. %0 Book Section %A Sali, A. %A Shakhnovich, E. %A Karplus, M. %D 1995 %T Protein Folding Studied by Monte Carlo Simulations %E Bohr, H. %E Brunak, S. %B Protein Folds: A Distance Based Approach %I CRC Press Inc. %P 202-216 %! Protein Folding Studied by Monte Carlo Simulations %L 42 %F 42 %K Protein Folding %Z TY - CHAP %U http://salilab.org/pdf/Sali_ProtFolds_1995.pdf %+ Florida, US %0 Book Section %A Sali, A. %A Shakhnovich, E. %A Karplus, M. %D 1995 %T Thermodynamics and kinetics of protein folding from lattice Monte Carlo simulations %E Shalloway, D. %E Xue, G. %E Pardalos, P. %B DIMACS Series in Discrete Mathematics and Theoretical Computer Science %I American Mathematical Society %V 23 %P 199-213 %! Thermodynamics and kinetics of protein folding from lattice Monte Carlo simulations %L 43 %F 43 %K Thermodynamics Kinetics Protein Folding Mathematics %Z TY - CHAP %U http://salilab.org/pdf/Sali_DIMACS_1995.pdf %0 Journal Article %A Sali, A. %A Turk, V. %D 1987 %T Prediction of the secondary structures of stefins and cystatins, the low-molecular mass protein inhibitors of cysteine proteinases %B Biol Chem Hoppe Seyler %V 368 %N 5 %P 493-499 %8 May %! Prediction of the secondary structures of stefins and cystatins, the low-molecular mass protein inhibitors of cysteine proteinases %M 3497644 %L 5 %F 5 %K Amino Acid Sequence Chemistry, Physical *Cystatins Cysteine Proteinase Inhibitors Molecular Weight Protease Inhibitors/*analysis Protein Conformation Proteins/*analysis %X A procedure for classifying proteins of known sequence into structurally similar groups was developed on the basis of the Argos parametric approach. It is shown that stefins and cystatins constitute two structurally well resolved, but homologous groups of proteins. Furthermore, it is very probable that segments of secondary structures within each family are conserved, although significant differences between stefins and cystatins are indicated at the level of secondary structure. Next, secondary structures of all sequenced stefins and cystatins were predicted and used in the construction of secondary structures of the "typical stefin" and the "typical cystatin". Results were interpreted in the light of evolution and inhibition mechanism: Alignment of the "typical stefin" versus the "typical cystatin" secondary structure segments suggests that the divergence of stefin and cystatin families did not occur by a gene fusion event, but only by a mechanism of substitution, insertion and/or deletion. The central region of low-molecular mass cystatins, which is assumed to interact with cysteine proteinases, is predicted to be in a beta-sheet conformation. This resembles the beta-sheet in the active site of "standard mechanism" serine proteinases inhibitors. %Z 0177-3593 Journal Article %U http://salilab.org/pdf/Sali_BiolChemHoppeSeyler_1987.pdf %0 Journal Article %A Sali, A. %A Veerapandian, B. %A Cooper, J. B. %A Foundling, S. I. %A Hoover, D. J. %A Blundell, T. L. %D 1989 %T High-resolution X-ray diffraction study of the complex between endothiapepsin and an oligopeptide inhibitor: the analysis of the inhibitor binding and description of the rigid body shift in the enzyme %B Embo Journal %V 8 %N 8 %P 2179-2188 %8 Aug %! High-resolution X-ray diffraction study of the complex between endothiapepsin and an oligopeptide inhibitor: the analysis of the inhibitor binding and description of the rigid body shift in the enzyme %M 2676515 %L 8 %F 8 %K *Aspartic Endopeptidases Binding Sites Chemistry Crystallography Endopeptidases/*metabolism Molecular Structure Oligopeptides/*metabolism Protease Inhibitors/*metabolism Protein Binding Protein Conformation Research Support, Non-U.S. Gov't X-Ray Diffraction %X The conformation of the synthetic renin inhibitor CP-69,799, bound to the active site of the fungal aspartic proteinase endothiapepsin (EC 3.4.23.6), has been determined by X-ray diffraction at 1.8 A resolution and refined to the crystallographic R factor of 16%. CP-69,799 is an oligopeptide transition--state analogue inhibitor that contains a new dipeptide isostere at the P1-P1' position. This dipeptide isostere is a nitrogen analogue of the well-explored hydroxyethylene dipeptide isostere, wherein the tetrahedral P1' C alpha atom has been replaced by trigonal nitrogen. The inhibitor binds in the extended conformation, filling S4 to S3' pockets, with hydroxyl group of the P1 residue positioned symmetrically between the two catalytic aspartates of the enzyme. Interactions between the inhibitor and the enzyme include 12 hydrogen bonds and extensive van der Waals contacts in all the pockets, except for S3'. The crystal structure reveals a bifurcated orientation of the P2 histidine side chain and an interesting relative rotation of the P3 phenyl ring to accommodate the cyclohexyl side chain at P1. The binding of the inhibitor to the enzyme, while producing no large distortions in the enzyme active site cleft, results in small but significant change in the relative orientation of the two endothiapepsin domains. This structural change may represent the action effected by the proteinase as it distorts its substrate towards the transition state for proteolytic cleavage. %Z 0261-4189 Journal Article %U http://salilab.org/pdf/Sali_EmboJournal_1989.pdf %+ Department of Crystallography, Birkbeck College, University of London, UK. %0 Journal Article %A Sali, A %A Lima, CD %A Kostic, M %D 2007 %T Structural genomics %B Structure %V 15 %N 11 %P 1341 %8 Nov %! Structural genomics. %@ 0969-2126 %M 17997955 %L 187 %F 187 %K Animals Databases, Protein Genomics Humans Proteins Structural Homology, Protein %U http://salilab.org/pdf/Sali_Structure_2007.pdf %G eng %0 Journal Article %A Sali, A %A Shakhnovich, E %A Karplus, M %D 1994 %T How does a protein fold? %B Nature %V 369 %N 6477 %P 248-251 %8 May %! How does a protein fold? %@ 0028-0836 %M 7710478 %L 34 %F 34 %K Models, Chemical Monte Carlo Method Protein Folding Thermodynamics %X The number of all possible conformations of a polypeptide chain is too large to be sampled exhaustively. Nevertheless, protein sequences do fold into unique native states in seconds (the Levinthal paradox). To determine how the Levinthal paradox is resolved, we use a lattice Monte Carlo model in which the global minimum (native state) is known. The necessary and sufficient condition for folding in this model is that the native state be a pronounced global minimum on the potential surface. This guarantees thermodynamic stability of the native state at a temperature where the chain does not get trapped in local minima. Folding starts by a rapid collapse from a random-coil state to a random semi-compact globule. It then proceeds by a slow, rate-determining search through the semi-compact states to find a transition state from which the chain folds rapidly to the native state. The elements of the folding mechanism that lead to the resolution of the Levinthal paradox are the reduced number of conformations that need to be searched in the semi-compact globule (approximately 10(10) versus approximately 10(16) for the random coil) and the existence of many (approximately 10(3)) transition states. The results have evolutionary implications and suggest principles for the folding of real proteins. %U http://salilab.org/pdf/Sali_Nature_1994.pdf %+ Department of Chemistry, Harvard University, Cambridge, Massachusetts 02138. %G eng %0 Journal Article %A Sali, A %A Veerapandian, B %A Cooper, JB %A Moss, DS %A Hofmann, T %A Blundell, TL %D 1992 %T Domain flexibility in aspartic proteinases %B Proteins %V 12 %N 2 %P 158-170 %8 Feb %! Domain flexibility in aspartic proteinases. %@ 0887-3585 %M 1603805 %L 23 %F 23 %K Aspartic Endopeptidases Binding Sites Chymosin Models, Molecular Molecular Structure Pepsin A Pepsinogens Protein Conformation Temperature X-Ray Diffraction %X Comparison of the three-dimensional structures of native endothiapepsin (EC 3.4.23.6) and 15 endothiapepsin oligopeptide inhibitor complexes defined at high resolution by X-ray crystallography shows that endothiapepsin exists in two forms differing in the relative orientation of a domain comprising residues 190-302. There are relatively few interactions between the two parts of the enzyme; consequently, they can move as separate rigid bodies. A translational, librational, and screw analysis of the thermal parameters of endothiapepsin also supports a model in which the two parts can move relative to each other. In the comparison of different aspartic proteinases, the rms values are reduced by up to 47% when the two parts of the structure are superposed independently. This justifies description of the differences, including those between pepsinogen and pepsin (EC 3.4.34.1), as a rigid movement of one part relative to another although considerable distortions within the domains also occur. The consequence of the rigid body movement is a change in the shape of the active site cleft that is largest around the S3 pocket. This is associated with a different position and conformation of the inhibitors that are bound to the two endothiapepsin forms. The relevance of these observations to a model of the hydrolysis by aspartic proteinases is briefly discussed. %U http://salilab.org/pdf/Sali_Proteins_1992.pdf %+ Department of Crystallography, Birkbeck College, University of London, England. %G eng %0 Journal Article %A Saltzberg, D. J. %A Hepburn, M. %A Pilla, K. B. %A Schriemer, D. C. %A Lees-Miller, S. P. %A Blundell, T. L. %A Sali, A. %D 2019 %T SSEThread: Integrative threading of the DNA-PKcs sequence based on data from chemical cross-linking and hydrogen deuterium exchange %B Prog Biophys Mol Biol %V 147 %P 92-102 %7 2019/09/27 %8 Sep %! SSEThread: Integrative threading of the DNA-PKcs sequence based on data from chemical cross-linking and hydrogen deuterium exchange %@ 1873-1732 %R 10.1016/j.pbiomolbio.2019.09.003 %2 PMCID6903780 %$ NIHMS1056592 %M 31570166 %L 399 %F 399 %K DNA-PKcs Electron microscopy Integrative modeling Threading X-ray crystallography %X X-ray crystallography and electron microscopy maps resolved to 3-8 Å are generally sufficient for tracing the path of the polypeptide chain in space, while often insufficient for unambiguously registering the sequence on the path (i.e., threading). Frequently, however, additional information is available from other biophysical experiments, physical principles, statistical analyses, and other prior models. Here, we formulate an integrative approach for sequence assignment to a partial backbone model as an optimization problem, which requires three main components: the representation of the system, the scoring function, and the optimization method. The method is implemented in the open source Integrative Modeling Platform (IMP) (https://integrativemodeling.org), allowing a number of different terms in the scoring function. We apply this method to localizing the sequence assignment within a 199-residue disordered region of three structured and sequence unassigned helices in the DNA-PKcs crystallographic structure, using chemical crosslinks, hydrogen deuterium exchange, and sequence connectivity. The resulting ensemble of threading models provides two major solutions, one of which suggests that the crucial ABCDE cluster of phosphorylation sites cannot undergo intra-molecular autophosphorylation without a conformational rearrangement. The ensemble of solutions embodies the most accurate and precise sequence threading given the available information. %U https://salilab.org/pdf/Saltzberg_ProgBiophysMolBiol_2019.pdf %G eng %0 Journal Article %A Saltzberg, D.J. %A Broughton, H.B. %A Pellarin, R. %A Chalmers, M.J. %A Espada, A. %A Dodge, J.A. %A Pascal, B.D. %A Griffin, P.R. %A Humblet, C. %A Sali, A. %D 2016 %T A Residue Resolved Bayesian Approach to Quantitative Interpretation of Hydrogen Deuterium Exchange from Mass Spectrometry: Application to Characterizing Protein-Ligand Interactions %B J Phys Chem B %V 121 %N 15 %P 3493-3501 %! A Residue Resolved Bayesian Approach to Quantitative Interpretation of Hydrogen Deuterium Exchange from Mass Spectrometry: Application to Characterizing Protein-Ligand Interactions %R 10.1021/acs.jpcb.6b09358 %2 PMCID5693600 %M 27807976 %L 359 %F 359 %U https://salilab.org/pdf/Saltzberg_JPhysChemB_2016.pdf %0 Journal Article %A Saltzberg, D.J. %A Viswanath, S. %A Echeverria, I. %A Chemmama, I.E. %A Webb, B. %A Sali, A. %D 2021 %T Using Integrative Modeling Platform to Compute, Validate, and Archive a Model of a Protein Complex Structure %B Prot Sci %V 30 %N 1 %P 250-261 %! Using Integrative Modeling Platform to Compute, Validate, and Archive a Model of a Protein Complex Structure %R 10.1002/pro.3995 %2 PMCID7737781 %M 33166013 %L 412 %F 412 %U https://salilab.org/pdf/Saltzberg_ProtSci_2021.pdf %0 Journal Article %A Saltzberg, D %A Greenberg, CH %A Viswanath, S %A Chemmama, I %A Webb, B %A Pellarin, R %A Echeverria, I %A Sali, A %D 2019 %T Modeling biological complexes using Integrative Modeling Platform %B Methods Mol Biol %V 2022 %P 353-377 %! Modeling biological complexes using Integrative Modeling Platform %R 10.1007/978-1-4939-9608-7_15 %M 31396911 %L 394 %F 394 %U https://salilab.org/pdf/Saltzberg_MethodsMolBiol_2019.pdf %0 Journal Article %A Sampathkumar, P. %A Gheyi, T. %A Miller, S.A. %A Bain, K.T. %A Dickey, M. %A Bonanno, J.B. %A S.J., Kim. %A Phillips, J. %A Pieper, U. %A Fernandez-Martinez, J. %A Franke, J. %A Martel, A. %A Tsuruta, H. %A Atwell, S. %A Thompson, D.A. %A Emtage, J.S. %A Wasserman, S.R. %A Rout, M.P. %A Sali, A. %A Sauder, J.M. %A Burley, S.K. %D 2011 %T Structure of the C-terminal domain of Saccharomyces cerevisiae Nup133, a component of the Nuclear Pore Complex %B Proteins:Struct Funct Bioinform %V 79 %P 1672-1677 %! Structure of the C-terminal domain of Saccharomyces cerevisiae Nup133, a component of the Nuclear Pore Complex %2 PMCID3350809 %M 21365675;PMCID:PMC3350809 %L 253 %F 253 %U http://salilab.org/pdf/Sampathkumar_Proteins-StructFunctBioinform_2011.pdf %0 Journal Article %A Sampathkumar, P. %A Kim, S.J. %A Manglicmot, D. %A Bain, K.T. %A Gilmore, J. %A Gheyi, T. %A Phillips, J. %A Pieper, U. %A Fernandez-Martinez, J. %A Franke, J. %A Matsui, T. %A Tsuruta, H. %A Atwell, S. %A Thompson, D. %A Emtage, J. S. %A Wasserman, S. %A Rout, M. %A Sali, A. %A Sauder, J.M. %A Almo, S.C. %A Burley, S.K. %D 2012 %T Atomic structure of the Nuclear Pore Complex targeting domain of Nup116 homologue from the yeast, Candida glabrata %B Proteins:Struct Funct Bioinform %V 8 %P 2110-2116 %! Atomic structure of the Nuclear Pore Complex targeting domain of Nup116 homologue from the yeast, Candida glabrata %2 PMCID3686472 %M 22544723;PMCID:PMC3686472 %L 275 %F 275 %U http://salilab.org/pdf/Sampathkumar_Proteins_2012.pdf %0 Journal Article %A Sampathkumar, P. %A Kim, S.J. %A Upla, P. %A Rice, W. %A Phillips, J. %A Timney, B. %A Pieper, U. %A Bonanno, J. %A Fernandez-Martinez, J. %A Hakhverdyan, Z. %A Ketaren, N. %A Matsui, T. %A Weiss, R. %A Stokes, D. %A Sauder, J.M. %A Burley, S. %A Sali, A. %A Rout, M. %A Almo, S. %D 2013 %T Structure, dynamics, evolution and function of a major scaffold component in the Nuclear Pore Complex %B Structure %V 21 %P 560-571 %! Structure, dynamics, evolution and function of a major scaffold component in the Nuclear Pore Complex %2 PMCID3755625 %M 23499021;PMCID:PMC3755625 %L 292 %F 292 %U http://salilab.org/pdf/Sampathkumar_Structure_2013.pdf %0 Journal Article %A Sampathkumar, P. %A Lu, F. %A Zhao, X. %A Li, Z. %A Gilmore, J. %A Bain, K. %A Rutter, M.E. %A Gheyi, T. %A Schwinn, K. %A Bonanno, J. %A Pieper, U. %A Fajardo, J.E. %A Fiser, A. %A Almo, S. %A Swaminathan, A. %A Chance, M. %A Baker, D. %A Atwell, S. %A Thompson, D. %A Emtage, J.S. %A Wasserman, S. %A Sali, A. %A Sauder, J.M. %A Burley, S. %D 2010 %T Structure of a putative BenF-like porin from Pseudomanas fluorescens Pf-5 at 2.6 Å resolution %B Proteins:Struct Funct Bioinform %V 78 %P 3056-3062 %! Structure of a putative BenF-like porin from Pseudomanas fluorescens Pf-5 at 2.6 Å resolution %2 PMCID2989796 %M 20737437;PMCID:PMC2989796 %L 243 %F 243 %U http://salilab.org/pdf/Sampathkumar_Proteins-StructFunctBioinform_2010a.pdf %0 Journal Article %A Sampathkumar, P. %A Ozyurt, S.A. %A Do, J. %A Bain, K.T. %A Dickey, M. %A Rodgers, L.A. %A Gheyi, T. %A Sali, A. %A Kim, S.J. %A Phillips, J. %A Pieper, U. %A Fernandez-Martinez, J. %A Franke, J.D. %A Martel, A. %A Tsuruta, H. %A Atwell, S. %A Thompson, D.A. %A Emtage, J.S. %A Wasserman, S.R. %A Rout, M.P. %A Sauder, J.M. %A Burley, S.K. %D 2010 %T Structures of the autoproteolytic domain from the Saccharomyces cerevisiae nuclear pore complex component, Nup 145 %B Proteins:Struct Funct Bioinform %V 78 %N 8 %P 1992-1998 %! Structures of the autoproteolytic domain from the Saccharomyces cerevisiae nuclear pore complex component, Nup 145 %2 PMCID3136511 %M 20310066;PMCID:PMC3136511 %L 242 %F 242 %U http://salilab.org/pdf/Sampathkumar_Proteins-StructFunctBioinform_2010.pdf %0 Journal Article %A Sanchez, R. %A Badretdinov, A.Ya. %A Feyfant, E. %A Sali, A. %D 1997 %T Homology protein structure modeling %B Transactions of the American Crystallographic Association %V 32 %P 81-91 %8 1997/// %! Homology protein structure modeling %L 60 %F 60 %Z TY - JOUR %U http://salilab.org/pdf/Sanchez_TransactionsoftheAmericanCrystallographicAssociation_1997.pdf %0 Journal Article %A Sanchez, R. %A Pieper, U. %A Melo, F. %A Eswar, N. %A Marti-Renom, M. A. %A Madhusudhan, M. S. %A Mirkovic, N. %A Sali, A. %D 2000 %T Protein structure modeling for structural genomics %B Nat Struct Biol %V 7 %P 986-990 %! Protein structure modeling for structural genomics %@ 1072-8368 %M 11104007 %L 88 %F 88 %X The shapes of most protein sequences will be modeled based on their similarity to experimentally determined protein structures. The current role, limitations, challenges and prospects for protein structure modeling (using information about genes and genomes) are discussed in the context of structural genomics. %Z S %U http://salilab.org/pdf/Sanchez_NatStructBiol_2000.pdf %0 Journal Article %A Sanchez, R. %A Pieper, U. %A Mirkovic, N. %A de Bakker, P. I. W. %A Wittenstein, E. %A Sali, A. %D 2000 %T ModBase, a database of annotated comparative protein structure models %B Nucleic Acids Res %V 28 %N 1 %P 250-253 %! ModBase, a database of annotated comparative protein structure models %@ 0305-1048 %M 10592238 %L 81 %F 81 %X MODBASE is a queryable database of annotated comparative protein structure models, The models are derived by MODPIPE, an automated modeling pipeline relying on the programs PSI-BLAST and MODELLER. The database currently contains 3D models for substantial portions of approximately 17 000 proteins from 10 complete genomes, including those of Caenorhabditis elegans, Saccharomyces cerevisiae and Escherichia coli, as well as all the available sequences from Arabidopsis thaliana and Homo sapiens, The database also includes fold assignments and alignments on which the models were based, In addition, special care is taken to assess the quality of the models, ModBase is accessible through a web interface at http://guitar.rockefeller. edu/modbase/. %U http://salilab.org/pdf/Sanchez_NucleicAcidsRes_2000.pdf %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1997 %T Advances in comparative protein-structure modelling %B Curr Opin Struct Biol %V 7 %N 2 %P 206-214 %8 Apr %! Advances in comparative protein-structure modelling %M 9094331 %L 55 %F 55 %K Crystallography, X-Ray Magnetic Resonance Spectroscopy *Models, Molecular *Protein Conformation Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Alignment Software %X Comparative modelling of protein 3D structure can now be applied with reasonable accuracy to ten times more protein sequences than the number of experimentally determined protein structures. A protein sequence that has at least 40% identity to a known structure can be modelled automatically with an accuracy approaching that of a low resolution X-ray structure or a medium resolution NMR structure. Currently, the errors in comparative models include mistakes in the packing of sidechains, in the conformation and shifts of the core segments and loops, and, most importantly, in an incorrect alignment of the modelled sequence with related known structures. Nevertheless, the number of applications in which comparative modelling has been proven to be useful has grown rapidly. %Z 0959-440x Journal Article Review %U http://salilab.org/pdf/Sanchez_CurrOpinStructBiol_1997.pdf %+ Box 270, The Rockefeller University 1230 York Avenue, New York, NY 10021-6399, USA. %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1997 %T Comparative protein modeling as an optimization problem %B Journal of Molecular Structure (Theochem) %V 398 %P 489-496 %8 1997/// %! Comparative protein modeling as an optimization problem %L 54 %F 54 %Z TY - JOUR %U http://salilab.org/pdf/Sanchez_JournalofMolecularStructure_1997.pdf %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1997 %T Evaluation of comparative protein structure modeling by MODELLER-3 %B Proteins %V Suppl 1 %P 50-58 %! Evaluation of comparative protein structure modeling by MODELLER-3 %M 9485495 %L 58 %F 58 %K Amino Acid Sequence Animals Comparative Study Crystallography, X-Ray Evaluation Studies Humans Ligases/*chemistry Mice *Models, Molecular Molecular Sequence Data Phosphotransferases/*chemistry *Protein Conformation Reproducibility of Results Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Alignment *Software Stereoisomerism Templates, Genetic Tetrahydrofolate Dehydrogenase/*chemistry *Ubiquitin-Conjugating Enzymes %X We evaluate homology-derived 3D models of dihydrofolate reductase (DFR1), phosphotransferase enzyme IIA domain (PTE2A3), and mouse/human UBC9 protein (UBC9(24)) which were submitted to the second Meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP). The DFR1 and PTE2A3 models, based on alignments without large errors, were slightly closer to their corresponding X-ray structures than the closest template structures. By contrast, the UBC9(24) model was slightly worse than the best template due to a misalignment of the N-terminal helix. Although the current models appear to be more accurate than the models submitted to the CASP meeting in 1994, the four major types of errors in side chain packing, position and conformation of aligned segments, position and conformation of inserted segments, and in alignment still occur to almost the same degree. The modest improvement probably originates from the careful manual selection of the templates and editing of the alignment, as well as from the iterative realignment and model building guided by various model evaluation techniques. This iterative approach to comparative modeling is likely to overcome at least some initial alignment errors, as demonstrated by the correct final alignment of the C terminus of DFR. %Z 0887-3585 Journal Article %U http://salilab.org/pdf/Sanchez_Proteins_1997.pdf %+ Rockefeller University, New York, New York 10021, USA. %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1998 %T Large-scale protein structure modeling of the Saccharomyces cerevisiae genome %B Proc Natl Acad Sci U S A %V 95 %N 23 %P 13597-13602 %8 Nov 10 %! Large-scale protein structure modeling of the Saccharomyces cerevisiae genome %M 9811845 %L 67 %F 67 %K Fungal Proteins/*chemistry *Genome, Fungal *Models, Molecular *Protein Conformation Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Saccharomyces cerevisiae/*genetics %X The function of a protein generally is determined by its three-dimensional (3D) structure. Thus, it would be useful to know the 3D structure of the thousands of protein sequences that are emerging from the many genome projects. To this end, fold assignment, comparative protein structure modeling, and model evaluation were automated completely. As an illustration, the method was applied to the proteins in the Saccharomyces cerevisiae (baker's yeast) genome. It resulted in all-atom 3D models for substantial segments of 1,071 (17%) of the yeast proteins, only 40 of which have had their 3D structure determined experimentally. Of the 1,071 modeled yeast proteins, 236 were related clearly to a protein of known structure for the first time; 41 of these previously have not been characterized at all. %Z 0027-8424 Journal Article %U http://salilab.org/pdf/Sanchez_ProcNatlAcadSciUSA_1998.pdf %+ Laboratories of Molecular Biophysics, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1999 %T Comparative protein structure modeling in genomics %B Journal of Computational Physics %V 151 %P 388-401 %8 1999/// %! Comparative protein structure modeling in genomics %L 70 %F 70 %K Genomics %Z TY - JOUR %U http://salilab.org/pdf/Sanchez_JournalofComputationalPhysics_1999.pdf %0 Journal Article %A Sanchez, R. %A Sali, A. %D 1999 %T MODBASE: A database of comparative protein structure models %B Bioinformatics %V 15 %N 12 %P 1060-1061 %! MODBASE: A database of comparative protein structure models %@ 1367-4803 %M 10745998 %L 69 %F 69 %X MODBASE is a database of evaluated and annotated comparative protein structure models. The database also includes fold assignments and alignments on which the models were based. %U http://salilab.org/pdf/Sanchez_Bioinformatics_1999.pdf %0 Journal Article %A Sanchez, R. %A Sali, A. %D 2000 %T Comparative protein structure modeling. Introduction and practical examples with modeller %B Methods Mol Biol %V 143 %P 97-129 %! Comparative protein structure modeling. Introduction and practical examples with modeller %M 11084904 %L 83 %F 83 %K *Amino Acid Sequence Carrier Proteins/chemistry/genetics Computer Simulation Databases Haloferax volcanii/enzymology Internet *Models, Molecular Molecular Sequence Data Nerve Tissue Proteins/chemistry/genetics *Protein Structure, Tertiary Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Sequence Alignment *Software Tetrahydrofolate Dehydrogenase/chemistry/genetics %Z 1064-3745 Journal Article %U http://salilab.org/pdf/Sanchez_MethodsMolBiol_2000.pdf %+ Laboratories of Molecular Biophysics, Rockefeller University, New York, NY, USA. %0 Journal Article %A Schlessinger, A. %A Geier, E. %A Fan, H. %A Irwin, J. %A Shoichet, B. %A Giacomini, K. %A Sali, A. %D 2011 %T Structure-based Discovery of Prescription Drugs that Interact with the Norepinephrine Transporter, NET %B Proc Natl Acad Sci USA %V 108 %P 15810-15815 %! Structure-based Discovery of Prescription Drugs that Interact with the Norepinephrine Transporter, NET %2 PMCID3179104 %M 21885739;PMCID:PMC3179104 %L 262 %F 262 %U http://salilab.org/pdf/Schlessinger_ProcNatlAcadSciUSA_2011.pdf %0 Journal Article %A Schlessinger, A. %A Khuri, N. %A Giacomini, K. %A Sali, A. %D 2013 %T Molecular modeling and ligand docking for Solute Carrier (SLC) transporters %B Curr Top Med Chem %V 13 %P 843-856 %! Molecular modeling and ligand docking for Solute Carrier (SLC) transporters %2 PMCID4056341 %M 23578028;PMCID:PMC4056341 %L 295 %F 295 %U http://salilab.org/pdf/Schlessinger_CurTopMedChem_2013a.pdf %0 Journal Article %A Schlessinger, A. %A Matsson, P. %A Shima, J.E. %A Pieper, U. %A Yee, S.W. %A Kelly, L. %A Apeltsin, L. %A Stroud, R.M. %A Ferrin, T.E. %A Giacomini, K.M. %A Sali, A. %D 2010 %T Comparison of Human Solute Carriers %B Protein Sci %V 19 %P 412-428 %! Comparison of Human Solute Carriers %2 PMCID2866268 %M 20052679;PMCID:PMC2866268 %L 239 %F 239 %U http://salilab.org/pdf/Schlessinger_ProteinSci_2010.pdf %0 Journal Article %A Schlessinger, A. %A Wittwer, M.B. %A Dahlin, A. %A Khuri, N. %A Bonomi, M. %A Fan, H. %A Giacomini, K. %A Sali, A. %D 2012 %T High Selectivity of the γ-Aminobutyric Acid Transporter 2 (GAT-2, SLC6A13) Revealed by Structure-based Approach %B J Biol Chem %V 287 %P 37745-37756 %! High Selectivity of the γ-Aminobutyric Acid Transporter 2 (GAT-2, SLC6A13) Revealed by Structure-based Approach %2 PMCID3488050 %M 22932902;PMCID:PMC3488050 %L 286 %F 286 %U http://salilab.org/pdf/Schlessinger_JBiolChem_2012.pdf %0 Journal Article %A Schlessinger, A. %A Yee, S.W. %A Sali, A. %A Giacomini, K. %D 2013 %T SLC classification: an update %B Clin Pharmacol Ther %V 94 %P 19-23 %! SLC classification: an update %2 PMCID4068830 %M 23778706;PMCID:PMC4068830 %L 302 %F 302 %U http://salilab.org/pdf/Schlessinger_ClinPharmacolTher_2013b.pdf %0 Journal Article %A Schneidman, D %A Hammel, M %A Tainer, J %A Sali, A %D 2016 %T FoXS, FoXSDock, and MultiFoXS: Single-state and multi-state structural modeling of proteins and their complexes based on SAXS profiles %B Nucleic Acids Res %V 44 %N W1 %P W424-9 %7 2016 May 5 %! FoXS, FoXSDock, and MultiFoXS: Single-state and multi-state structural modeling of proteins and their complexes based on SAXS profiles %R 10.1093/nar/gkw389 %2 PMCID4987932 %M 27151198 %L 352 %F 352 %U https://salilab.org/pdf/Schneidman-Duhovny_NucleicAcidsRes_2016.pdf %W https://github.com/salilab/multifoxs; https://github.com/salilab/foxsdock %0 Journal Article %A Schneidman-Duhovny, D. %A Hammel, M. %A Sali, A. %D 2010 %T FoXS: A Web Server for Rapid Computation and Fitting of SAXS Profiles %B Nucleic Acids Res %V 38 %P 541-544 %! FoXS: A Web Server for Rapid Computation and Fitting of SAXS Profiles %2 PMCID2896111 %M 20507903;PMCID:PMC2896111 %L 240 %F 240 %U http://salilab.org/pdf/Schneidman-Duhovny_NucleicAcidsRes_2010.pdf %0 Journal Article %A Schneidman-Duhovny, D. %A Hammel, M. %A Sali, A. %D 2011 %T Macromolecular docking restrained by a small angle X-ray scattering profile %B J Struct Biol %V 3 %P 461-471 %! Macromolecular docking restrained by a small angle X-ray scattering profile %2 PMCID3040266 %M 20920583;PMCID:PMC3040266 %L 248 %F 248 %U http://salilab.org/pdf/Schneidman-Duhovny_JStructBiol_2010a.pdf %0 Journal Article %A Schneidman-Duhovny, D. %A Hammel, M. %A Tainer, J. %A Sali, A. %D 2013 %T Accurate SAXS profile computation and its assessment by contrast variation experiments %B Biophys J %V 105 %P 962-974 %! Accurate SAXS profile computation and its assessment by contrast variation experiments %2 PMCID3752106 %M 23972848 ;PMCID:PMC3752106 %L 304 %F 304 %U http://salilab.org/pdf/Schneidman-Duhovny_BiophysJ_2013.pdf %0 Journal Article %A Schneidman-Duhovny, D. %A Khuri, N. %A Dong, G.Q. %A Winter, M.B. %A Shifrut, E. %A Friedman, N. %A Craik, C.S. %A Pratt, K.P. %A Paz, P. %A Aswad, F. %A Sali, A. %D 2018 %T Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition %B PLoS ONE %V 13 %N 11 %P e0206654 %! Predicting CD4 T-cell epitopes based on antigen cleavage, MHCII presentation, and TCR recognition %R 10.1101/415661 %2 PMCID6219782 %M 30399156 %L 379 %F 379 %U https://salilab.org/pdf/Schneidman-Duhovny_PLosOne_2018.pdf %W https://github.com/salilab/itcell-lib %0 Journal Article %A Schneidman-Duhovny, D. %A Kim, S.J. %A Sali, A. %D 2012 %T Integrative structural modeling with small angle X-ray scattering profiles %B BMC Struct Biol %V 12 %P 17 %! Integrative structural modeling with small angle X-ray scattering profiles %2 PMCID3427135 %M 22800408;PMCID:PMC3427135 %L 287 %F 287 %U http://salilab.org/pdf/Schneidman-Duhovny_BMCStructBiol_2012a.pdf %0 Journal Article %A Schneidman-Duhovny, D. %A Pellarin, R. %A Sali, A. %D 2014 %T Uncertainty in Integrative Structural Modeling %B Curr Opin Struct Biol %V 28 %P 96-104 %! Uncertainty in Integrative Structural Modeling %2 PMCID4252396 %M 25173450;PMCID:PMC4252396 %L 326 %F 326 %U http://salilab.org/pdf/Schneidman-Duhovny_CurrOpinStructBiol_2014.pdf %0 Journal Article %A Schneidman-Duhovny, D. %A Rossi, A. %A Avila-Sakar, A. %A Kim, S.J. %A Velazquez-Muriel, J. %A Strop, P. %A Liang, H. %A Krukenberg, K.A. %A Liao, M. %A Kim, H.M. %A Sobhanifar, S. %A Dotsch, V. %A Raipal, A. %A Pons, J. %A Agard, D.A. %A Cheng, Y. %A Sali, A. %D 2012 %T A Method for Integrative Structure Determination of Protein-Protein Complexes %B Bioinformatics %V 28 %P 3282-3289 %! A Method for Integrative Structure Determination of Protein-Protein Complexes %2 PMCID3519461 %M 23093611;PMCID:PMC3519461 %L 277 %F 277 %U http://salilab.org/pdf/Schneidman_Bioinformatics_2012.pdf %0 Journal Article %A Schulze-Gahmen, U. %A Echeverria, I. %A Stjepanovic, G. %A Bai, Y. %A Lu, H. %A Schneidman-Duhovny, D. %A Doudna, J. A. %A Zhou, Q. %A Sali, A. %A Hurley, J. H. %D 2016 %T Insights into HIV-1 proviral transcription from the structure and dynamics of the Tat:AFF4:P-TEFb:TAR complex %B eLife %V 5 %P e15910 %! Insights into HIV-1 proviral transcription from the structure and dynamics of the Tat:AFF4:P-TEFb:TAR complex %R 10.7554/eLife.15910 %2 PMCID5072841 %M 27731797 %L 356 %F 356 %U https://salilab.org/pdf/SchulzeGahmen_eLife_2016.pdf %0 Book Section %A Schwede, T. %A Sali, A. %A Eswar, N. %A Peitsch, M.C. %D 2008 %T Protein Structure Modeling %E Schwede, T. %E Peitsch, M. C. %B Computational Structural Biology %C Singapore %I World Scientific Publishing Ltd. %P 3-35 %! Protein Structure Modeling %L 193 %F 193 %Z 1 %U http://salilab.org/pdf/Schwede_CompStrBiol_2008.pdf %0 Journal Article %A Schwede, T %A Sali, A %A Honig, B %A Levitt, M %A Berman, HM %A Jones, D %A Brenner, SE %A Burley, SK %A Das, R %A Dokholyan, NV %A Dunbrack, RL Jr %A Fidelis, K %A Fiser, A %A Godzik, A %A Huang, YJ %A Humblet, C %A Jacobson, MP %A Joachimiak, A %A Krystek, SR Jr %A Kortemme, T %A Kryshtafovych, A %A Montelione, GT %A Moult, J %A Murray, D %A Sanchez, R %A Sosnick, TR %A Standley, DM %A Stouch, T %A Vajda, S %A Vasquez, M %A Westbrook, JD %A Wilson, IA %D 2009 %T Outcome of a workshop on applications of protein models in biomedical research %B Structure %V 17 %N 2 %P 151-159 %8 Feb %! Outcome of a workshop on applications of protein models in biomedical research. %@ 0969-2126 %2 PMCID2739730 %M 19217386;PMCID:PMC2739730 %L 219 %F 219 %X We describe the proceedings and conclusions from the "Workshop on Applications of Protein Models in Biomedical Research" (the Workshop) that was held at the University of California, San Francisco on 11 and 12 July, 2008. At the Workshop, international scientists involved with structure modeling explored (i) how models are currently used in biomedical research, (ii) the requirements and challenges for different applications, and (iii) how the interaction between the computational and experimental research communities could be strengthened to advance the field. %U http://salilab.org/pdf/Schwede_Structure_2009.pdf %+ Swiss Institute of Bioinformatics, Biozentrum, University of Basel, Klingelbergstrasse 50-70, CH-4056 Basel, Switzerland. %G eng %0 Journal Article %A Serysheva, II %A Ludtke, SJ %A Baker, ML %A Cong, Y %A Topf, M %A Eramian, D %A Sali, A %A Hamilton, SL %A Chiu, W %D 2008 %T Subnanometer-resolution electron cryomicroscopy-based domain models for the cytoplasmic region of skeletal muscle RyR channel %B Proc Natl Acad Sci U S A %V 105 %N 28 %P 9610-9615 %8 Jul %! Subnanometer-resolution electron cryomicroscopy-based domain models for the cytoplasmic region of skeletal muscle RyR channel. %@ 1091-6490 %2 PMCID2474495 %M 18621707;PMCID:PMC2474495 %L 210 %F 210 %K Cryoelectron Microscopy Cytoplasm Models, Molecular Muscle, Skeletal Protein Structure, Secondary Protein Structure, Tertiary Ryanodine Receptor Calcium Release Channel %X The skeletal muscle Ca(2+) release channel (RyR1), a homotetramer, regulates the release of Ca(2+) from the sarcoplasmic reticulum to initiate muscle contraction. In this work, we have delineated the RyR1 monomer boundaries in a subnanometer-resolution electron cryomicroscopy (cryo-EM) density map. In the cytoplasmic region of each RyR1 monomer, 36 alpha-helices and 7 beta-sheets can be resolved. A beta-sheet was also identified close to the membrane-spanning region that resembles the cytoplasmic pore structures of inward rectifier K(+) channels. Three structural folds, generated for amino acids 12-565 using comparative modeling and cryo-EM density fitting, localize close to regions implicated in communication with the voltage sensor in the transverse tubules. Eleven of the 15 disease-related residues for these domains are mapped to the surface of these models. Four disease-related residues are found in a basin at the interfaces of these regions, creating a pocket in which the immunophilin FKBP12 can fit. Taken together, these results provide a structural context for both channel gating and the consequences of certain malignant hyperthermia and central core disease-associated mutations in RyR1. %U http://salilab.org/pdf/Serysheva_ProcNatlAcadSciUSA_2008.pdf %+ National Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA. %G eng %0 Journal Article %A Shen, M. Y. %A Sali, A. %D 2006 %T Statistical potential for assessment and prediction of protein structures %B Protein Sci %V 15 %N 11 %P 2507-2524 %8 Nov %! Statistical potential for assessment and prediction of protein structures %M 17075131 %L 171 %F 171 %K Computational Biology/*methods Crystallography, X-Ray Data Interpretation, Statistical Models, Molecular *Models, Theoretical Protein Folding *Protein Structure, Secondary Sensitivity and Specificity %X Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance-dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non-native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo-electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER-8. %Z 0961-8368 (Print) Comparative Study Evaluation Studies Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't %U http://salilab.org/pdf/Shen_ProteinSci_2006.pdf %+ Department of Biopharmaceutical Sciences, Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California 94158, USA. smy@salilab.org %0 Journal Article %A Shen, MY %A Davis, FP %A Sali, A %D 2005 %T The optimal size of a globular protein domain: A simple sphere-packing model %B Chem Phys Lett %V 405 %N 1-3 %P 224-228 %8 MAR 31 2005 %! The optimal size of a globular protein domain: A simple sphere-packing model %@ 0009-2614 %L 153 %F 153 %K ASTRAL COMPENDIUM SEQUENCE MOLECULES CLASSIFICATION STABILITY EVOLUTION %X We describe a model that relates the optimal size of a globular protein domain to the ratio between hydrophilic and hydrophobic amino acid residues. This model represents a domain as a homogeneous spherical assembly of monodisperse spheres corresponding to the individual residues; the hydrophilic spheres are distributed on the assembly surface, and the hydrophobic spheres are buried in the core. The model predicts that a domain with a 1: 1 ratio of hydrophilic and hydrophobic residues is composed of 156 residues. It also predicts that smaller protein domains have more hydrophilic, than hydrophobic residues. These predictions are in agreement with the distribution of domain size and residue composition for the experimentally determined protein structures. (c) 2005 Elsevier B.V. All rights reserved. %Z Cited References Count:29 %U http://salilab.org/pdf/Shen_ChemicalPhysicsLetters_2005.pdf %+ Shen, MY (reprint author), Univ Calif San Francisco, Calif Inst Quantitat Biomed Res, Dept Biopharmaceut Sci, Miss Bay Genentech Hall,600 16th St, San Francisco, CA 94143 USA Univ Calif San Francisco, Calif Inst Quantitat Biomed Res, Dept Biopharmaceut Sci, San Francisco, CA 94143 USA Univ Calif San Francisco, Calif Inst Quantitat Biomed Res, Dept Pharmaceut Chem, San Francisco, CA 94143 USA %G English %0 Journal Article %A Sheng, Y. %A Krilis, S. A. %A Sali, A. %D 1997 %T Site-directed mutagenesis of recombinant human beta 2-glycoprotein I. Effect of phospholipid binding and anticardiolipin antibody activity %B Ann N Y Acad Sci %V 815 %P 331-333 %8 Apr 5 %! Site-directed mutagenesis of recombinant human beta 2-glycoprotein I. Effect of phospholipid binding and anticardiolipin antibody activity %M 9186672 %L 61 %F 61 %K Antibodies, Anticardiolipin/*immunology Binding Sites Enzyme-Linked Immunosorbent Assay Glycoproteins/*genetics/immunology/metabolism Humans Lysine Models, Molecular Mutagenesis, Site-Directed Phospholipids/*metabolism Recombinant Proteins/*genetics/immunology/metabolism %Z 0077-8923 Journal Article %U http://salilab.org/pdf/Sheng_AnnNYAcadSci_1997.pdf %+ Department of Immunology, Allergy, and Infectious Disease, University of New South Wales, St. George Hospital, Kogarah, Australia. %0 Journal Article %A Sheng, Y. %A Sali, A. %A Herzog, H. %A Lahnstein, J. %A Krilis, S. A. %D 1996 %T Site-directed mutagenesis of recombinant human beta 2-glycoprotein I identifies a cluster of lysine residues that are critical for phospholipid binding and anti-cardiolipin antibody activity %B J Immunol %V 157 %N 8 %P 3744-3751 %8 Oct 15 %! Site-directed mutagenesis of recombinant human beta 2-glycoprotein I identifies a cluster of lysine residues that are critical for phospholipid binding and anti-cardiolipin antibody activity %M 8871678 %L 47 %F 47 %K Amino Acid Sequence Animals Antibodies, Anticardiolipin/*metabolism Baculoviridae/genetics Base Sequence Binding Sites/genetics Cell Line Comparative Study DNA Primers/genetics Electrostatics Glycoproteins/genetics/*immunology/*metabolism Humans In Vitro Lysine/chemistry Models, Molecular Molecular Sequence Data Mutagenesis, Site-Directed Phospholipids/*metabolism Point Mutation Protein Binding Protein Conformation Recombinant Proteins/genetics/immunology/metabolism Research Support, Non-U.S. Gov't Spodoptera %X beta2-Glycoprotein I (beta2GPI) is a phospholipid-binding serum protein with anticoagulant properties. It plays a vital role in the binding of anti-cardiolipin Abs purified from patients with autoimmune disease when assayed in a cardiolipin (CL) ELISA. Based on a three-dimensional model of beta2GPI, electrostatic calculations, and earlier peptide studies, a highly positively charged amino acid sequence, Lys282-Asn-Lys-Glu-Lys-Lys287, located in the fifth domain of beta2GPI, has been predicted to be the phospholipid binding site. We tested this hypothesis by site-directed mutagenesis of residues in the predicted phospholipid binding site and by assessing the mutants for phospholipid binding and anti-beta2GPI activity. A single amino acid change from Lys286 to Glu significantly decreased the binding of beta2GPI to CL. Double and triple mutants 2k (from Lys286, 287 to Glu286, 287), 2ka (from Lys284, 287 to Glu284, 287), and 3k (from Lys284, 286, 287 to Glu284, 286, 287) possessed no binding of Ab to beta2GPI in a CL ELISA, as well as no inhibitory activity on the binding of iodinated native beta2GPI to CL. These results indicate that the residues Lys284, Lys286, and Lys287 in the fifth domain of beta2GPI are critical for its binding to anionic phospholipids and its subsequent capture for binding of anti-beta2GPI Abs. %Z 0022-1767 Journal Article %U http://salilab.org/pdf/Sheng_JImmunol_1996.pdf %+ Department of Immunology, Allergy, and Infectious Disease, University of New South Wales, The St. George Hospital, Kogarah, Australia. %0 Journal Article %A Shi, Y. %A Fernandez-Martinez, J. %A Tjioe, E. %A Pellarin, R. %A Kim, S.J. %A Williams, R. %A Schneidman, D. %A Sali, A. %A Rout, M. %A Chait, B. %D 2014 %T Structural characterization by cross-linking reveals the detailed architecture of a coatomer-related heptameric module from the nuclear pore complex %B Mol Cell Proteomics %V 13 %P 2927-2943 %! Structural characterization by cross-linking reveals the detailed architecture of a coatomer-related heptameric module from the nuclear pore complex %2 PMCID4223482 %M 25161197;PMCID:PMC4223482 %L 330 %F 330 %U http://salilab.org/pdf/Shi_MolCellProteomics_2014.pdf %0 Journal Article %A Shi, Y. %A Pellarin, R. %A Fridy, P. %A Fernandez-Martinez, J. %A Thompson, M. %A Li, Y. %A Wang, Q.J. %A Sali, A. %A Rout, M. %A Chait, B. %D 2015 %T A strategy for dissecting the architectures of native macromolecular assemblies. %B Nat Methods %V 12 %N 12 %P 1135-8 %7 2015 Oct 5 %! A strategy for dissecting the architectures of native macromolecular assemblies. %2 PMCID4803312 %M 26436480;PMCID:PMC4803312 %L 338 %F 338 %U http://salilab.org/pdf/Shi_NatMethods_2015.pdf %0 Journal Article %A Singh, D. %A Soni, N. %A Hutchings, J. %A Echeverria, I. %A Shaikh, F. %A Duquette, M. %A Suslov, S. %A Li, Z. %A van Eeuwen, T. %A Molloy, K. %A Shi, Y. %A Wang, J. %A Guo, Q. %A Chait, B.T. %A Fernandez-Martinez, J. %A Rout, M.P. %A Sali, A. %A Villa, E. %D 2024 %T The Molecular Architecture of the Nuclear Basket %B Cell %V 187 %N 19 %P 5267-5281 %! The Molecular Architecture of the Nuclear Basket %R 10.1016/j.cell.2024.07.020 %2 PMCID11416316 %M 39127037 %L 455 %F 455 %0 Journal Article %A Singla, J %A McClary, KM %A White, KL %A Alber, F %A Sali, A %A Stevens, RC %D 2018 %T Opportunities and challenges in building a spatiotemporal multi-scale model of the human pancreatic β-cell %B Cell %V 173 %N 1 %P 11-19 %! Opportunities and challenges in building a spatiotemporal multi-scale model of the human pancreatic β-cell %R 10.1016/j.cell.2018.03.014 %2 PMCID6014618 %M 29570991 %L 386 %F 386 %U https://salilab.org/pdf/Singla_Cell_2018.pdf %0 Journal Article %A Smith, C. %A Lin, K. %A Stecula, A. %A Sali, A. %A Shah, N. %D 2015 %T FLT3 D835 mutations confer differential resistance to type II FLT3 inhibitors %B Leukemia %V 29 %N 12 %P 2390-2 %7 2015 Jun 25 %! FLT3 D835 mutations confer differential resistance to type II FLT3 inhibitors %2 PMCID4675689 %M 26108694 %L 342 %F 342 %U http://salilab.org/pdf/Smith_Leukemia_2015.pdf %0 Journal Article %A Soranzo, N %A Kelly, L %A Martinian, L %A Burley, MW %A Thom, M %A Sali, A %A Kroetz, DL %A Goldstein, DB %A Sisodiya, SM %D 2007 %T Lack of support for a role for RLIP76 (RALBP1) in response to treatment or predisposition to epilepsy %B Epilepsia %V 48 %N 4 %P 674-683 %8 Apr %! Lack of support for a role for RLIP76 (RALBP1) in response to treatment or predisposition to epilepsy. %@ 0013-9580 %M 17437410 %L 179 %F 179 %K ATP-Binding Cassette Transporters Anticonvulsants Blood-Brain Barrier Carbamazepine Drug Resistance, Multiple Epilepsy Fluorescent Antibody Technique GTPase-Activating Proteins Genetic Predisposition to Disease Genetic Variation Genotype Haplotypes Humans Immunohistochemistry Multidrug Resistance-Associated Proteins P-Glycoprotein Pharmacogenetics Phenotype Phenytoin Polymorphism, Single Nucleotide %X BACKGROUND: Multidrug transporters are postulated to contribute to antiepileptic drug (AED) resistance. The transporter best studied is P-glycoprotein, an ATP-Binding Cassette (ABC) transporter superfamily member. RLIP76 is suggested to be an energy-dependent non-ABC transporter, reducing AED blood-brain barrier penetration, with a more important role than P-glycoprotein. Knowledge of which transporters may be critical in drug resistance is important for design of potential therapies. We tested the hypothesis that RLIP76 mediates AED resistance using methods complementary to those in the original report. METHODS: Double-labeling fluorescent immunohistochemistry localized RLIP76 expression. Population genetics was used to explore association of variation in the RLIP76-encoding gene with drug-response and epilepsy phenotypes. Comparative protein structure modeling and bioinformatic annotation were used to predict RLIP76 structure and features. RESULTS: In normal and epileptogenic brain tissue, immunoreactivity for RLIP76 was cytoplasmic, with colocalization with a neuronal, but not an endothelial, marker. Genotyping of six tagging SNPs, representing common genetic variation in RLIP76, in patients with epilepsy responsive (n = 262) or resistant (n = 107) to AEDs showed no association with phenotype at any level. RLIP76 genotypic and haplotypic frequencies in 783 patients with epilepsy and 359 healthy controls showed no association with epilepsy susceptibility. RLIP76 is not predicted to have transmembrane localization or ATPase activity. CONCLUSIONS: No support for RLIP76 itself in directly mediating resistance to AEDs nor in increasing susceptibility to epilepsy was found. More evidence is required before either a role for RLIP76 in drug resistance can be accepted or focus directed away from other transporters, such as P-glycoprotein. %U http://salilab.org/pdf/Soranzo_Epilepsia_2007.pdf %+ Department of Clinical and Experimental Epilepsy, Institute of Neurology, UCL, London, UK. %G eng %0 Journal Article %A Spahn, C. M. %A Beckmann, R. %A Eswar, N. %A Penczek, P. A. %A Sali, A. %A Blobel, G. %A Frank, J. %D 2001 %T Structure of the 80S ribosome from Saccharomyces cerevisiae--tRNA-ribosome and subunit-subunit interactions %B Cell %V 107 %N 3 %P 373-386 %8 Nov 2 %! Structure of the 80S ribosome from Saccharomyces cerevisiae--tRNA-ribosome and subunit-subunit interactions %M 11701127 %L 98 %F 98 %K Base Sequence Binding Sites Cryoelectron Microscopy/methods Models, Molecular Molecular Sequence Data *Nucleic Acid Conformation Rna RNA, Fungal/*chemistry/metabolism RNA, Ribosomal/chemistry RNA, Ribosomal, 18S/chemistry RNA, Ribosomal, 5.8S/chemistry RNA, Transfer/*chemistry/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Ribosomes/metabolism/*ultrastructure Saccharomyces cerevisiae/genetics %X A cryo-EM reconstruction of the translating yeast 80S ribosome was analyzed. Computationally separated rRNA and protein densities were used for docking of appropriately modified rRNA models and homology models of yeast ribosomal proteins. The core of the ribosome shows a remarkable degree of conservation. However, some significant differences in functionally important regions and dramatic changes in the periphery due to expansion segments and additional ribosomal proteins are evident. As in the bacterial ribosome, bridges between the subunits are mainly formed by RNA contacts. Four new bridges are present at the periphery. The position of the P site tRNA coincides precisely with its prokaryotic counterpart, with mainly rRNA contributing to its molecular environment. This analysis presents an exhaustive inventory of an eukaryotic ribosome at the molecular level. %Z 0092-8674 Journal Article %U http://salilab.org/pdf/Spahn_Cell_2001.pdf %+ Howard Hughes Medical Institute, Health Research Inc., Albany, NY 12201, USA. %0 Journal Article %A Spill, Y. %A Kim, S.J. %A Schneidman-Duhovny, D. %A Russel, D. %A Webb, B. %A Sali, A. %A Nilges, M. %D 2014 %T SAXS Merge: an automated statistical method to merge SAXS profiles using Gaussian processes %B J Synchrotron Radiat %V 21 %P 203-208 %! SAXS Merge: an automated statistical method to merge SAXS profiles using Gaussian processes %2 PMCID3874021 %M 24365937;PMCID:PMC3874021 %L 320 %F 320 %U http://salilab.org/pdf/Spill_JSynchrotronRadiat_2014.pdf %W https://github.com/salilab/saxsmerge %0 Journal Article %A Sprowl, J. %A Ong, S. S. %A Gibson, A. %A Hu, S. %A Du, G. %A Lin, W. %A Li, L. %A Bharill, S. %A Ness, R. %A Stecula, A. %A Offer, S. %A Diasio, R. %A Nies, A. %A Schwab, M. %A Cavaletti, G. %A Schlatter, E. %A Ciarimboli, G. %A Schellens, J. %A Isacoff, E. %A Sali, A. %A Chen, T. %A Baker, S. %A Sparreboom, A. %A Pabla, N. %D 2016 %T A phosphotyrosine switch regulates organic cation transporters %B Nat Commun %V 7 %P 10880 %! A phosphotyrosine switch regulates organic cation transporters %R 10.1038/ncomms10880 %2 PMCID4799362 %M 26979622 %L 349 %F 349 %U https://salilab.org/pdf/Sprowl_NatCommun_2016.pdf %0 Journal Article %A Stecula, A %A Schlessinger, A %A Giacomini, K %A Sali, A %D 2017 %T Human concentrative nucleoside transporter 3 (hCNT3, SLC28A3) forms a cyclic homotrimer %B Biochemistry %V 56 %N 27 %P 3475-3483 %! Human concentrative nucleoside transporter 3 (hCNT3, SLC28A3) forms a cyclic homotrimer %R 10.1021/acs.biochem.7b00339 %2 PMCID6917504 %M 28661652 %L 372 %F 372 %U https://salilab.org/pdf/Stecula_Biochemistry_2017.pdf %0 Journal Article %A Street, T. %A Zeng, X. %A Pellarin, R. %A Bonomi, M. %A Sali, A. %A Kelly, M. %A Chu, F. %A Agard, D. %D 2014 %T Elucidating the mechanism of substrate recognition by the bacterial Hsp90 molecular chaperone %B J Mol Biol %V 12 %P 2393-2404 %! Elucidating the mechanism of substrate recognition by the bacterial Hsp90 molecular chaperone %2 PMCID5322795 %M 24726919 %L 323 %F 323 %U http://salilab.org/pdf/Street_JMolBiol_2014.pdf %0 Journal Article %A Stroud, RM %A Choe, S %A Holton, J %A Kaback, HR %A Kwiatkowski, W %A Minor, DL %A Riek, R %A Sali, A %A Stahlberg, H %A Harries, W %D 2009 %T 2007 Annual progress report synopsis of the Center for Structures of Membrane Proteins %B J Struct Funct Genom %V 10 %N 2 %P 193-208 %8 Jan %! 2007 Annual progress report synopsis of the Center for Structures of Membrane Proteins. %@ 1345-711X %2 PMCID2705707 %M 19148774;PMCID:PMC2705707 %L 222 %F 222 %X A synopsis of the 2007 annual progress report for the Center for Structures of Membrane Proteins, a specialized center of the Protein Structure Initiative. %U http://salilab.org/pdf/Stroud_JStructFunctGenom_2009.pdf %+ The Center for Structures of Membrane Proteins (CSMP), UCSF, MC2240, S412C, UCSF-Genentech Hall, 600 16th Street, San Francisco, CA, 94158-2517, USA, stroud@msg.ucsf.edu. %G Eng %0 Journal Article %A Stuart, A. C. %A Ilyin, V. A. %A Sali, A. %D 2002 %T LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures %B Bioinformatics %V 18 %N 1 %P 200-201 %! LigBase: a database of families of aligned ligand binding sites in known protein sequences and structures %@ 1367-4803 %M 11836232 %L 106 %F 106 %X Sum: A database comprising all ligand-binding sites of known structure aligned with all related protein sequences and structures is described. Currently, the database contains approximately 50000 ligand-binding sites for small molecules found in the Protein Data Bank (PDB). The structure-structure alignments are obtained by the Combinatorial Extension (CE) program (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998) and sequence-structure alignments are extracted from the ModBase database of comparative protein structure models for all known protein sequences (Sanchez et aL, Nucleic Acids Res., 28, 250-253, 2000). It is possible to search for binding sites in LigBase by a variety of criteria. LigBase reports summarize ligand data including relevant structural information from the PDB file, such as ligand type and size, and contain links to all related protein sequences in the TrEMBL database. Residues in the binding sites are graphically depicted for comparison with other structurally defined family members. LigBase provides a resource for the analysis of families of related binding sites. Availability: LigBase is accessible on the web at http: //guitar.rockefeller.edu/ligbase. Contact: ash@guitar. rockefeller.edu; sali@rockefeller.edu. %U http://salilab.org/pdf/Stuart_Bioinformatics_2002.pdf %0 Journal Article %A Taylor, D.J. %A Devkota, B %A Huang, A.D. %A Topf, M. %A Eswar, N. %A Sali, A. %A Harvey, S.C. %A Frank, J. %D 2009 %T Comprehensive Molecular Structure of the Eukaryotic Ribosome %B Structure %V 17 %P 1591-1604 %! Comprehensive Atomic Model of the Eukaryotic Ribosome %2 PMCID2814252 %M 20004163;PMCID:PMC2814252 %L 231 %F 231 %U http://salilab.org/pdf/Taylor_Structure_2009.pdf %0 Journal Article %A Timney, B.L. %A Raveh, B. %A Mironska, R. %A Trivedi, J.M. %A Kim, S.J. %A Russel, D. %A Wente, S.R. %A Sali, A. %A Rout, M.P. %D 2016 %T Simple rules for passive diffusion through the nuclear pore complex %B J Cell Biol %V 215 %N 1 %P 57-76 %! Simple rules for passive diffusion through the nuclear pore complex %2 PMCID5057280 %M 27697925 %L 357 %F 357 %U https://salilab.org/pdf/Timney_JCellBiol_2016.pdf %0 Journal Article %A Tjioe, E. %A Lasker, K. %A Webb, B. %A Wolfson, H. %A Sali, A. %D 2011 %T MultiFit: A web server for fitting multiple protein structures into their electron microscopy density map %B Nucleic Acids Res %V 39 %P 167-170 %! MultiFit: A web server for fitting multiple protein structures into their electron microscopy density map %2 PMCID3125811 %M 21715383;PMCID:PMC3125811 %L 260 %F 260 %U http://salilab.org/pdf/Tjioe_NucleicAcidsRes_2011.pdf %0 Journal Article %A Topf, M. %A Baker, M. L. %A Marti-Renom, M. A. %A Chiu, W. %A Sali, A. %D 2006 %T Refinement of protein structures by iterative comparative modeling and CryoEM density fitting %B J Mol Biol %V 357 %N 5 %P 1655-1668 %8 Apr 14 %! Refinement of protein structures by iterative comparative modeling and CryoEM density fitting %M 16490207 %L 161 %F 161 %K Amino Acid Sequence Cryoelectron Microscopy *Models, Molecular Molecular Sequence Data Plant Viruses/chemistry *Protein Conformation Software Viral Proteins/*chemistry/genetics %X We developed a method for structure characterization of assembly components by iterative comparative protein structure modeling and fitting into cryo-electron microscopy (cryoEM) density maps. Specifically, we calculate a comparative model of a given component by considering many alternative alignments between the target sequence and a related template structure while optimizing the fit of a model into the corresponding density map. The method relies on the previously developed Moulder protocol that iterates over alignment, model building, and model assessment. The protocol was benchmarked using 20 varied target-template pairs of known structures with less than 30% sequence identity and corresponding simulated density maps at resolutions from 5A to 25A. Relative to the models based on the best existing sequence profile alignment methods, the percentage of C(alpha) atoms that are within 5A of the corresponding C(alpha) atoms in the superposed native structure increases on average from 52% to 66%, which is half-way between the starting models and the models from the best possible alignments (82%). The test also reveals that despite the improvements in the accuracy of the fitness function, this function is still the bottleneck in reducing the remaining errors. To demonstrate the usefulness of the protocol, we applied it to the upper domain of the P8 capsid protein of rice dwarf virus that has been studied by cryoEM at 6.8A. The C(alpha) root-mean-square deviation of the model based on the remotely related template, bluetongue virus VP7, improved from 8.7A to 6.0A, while the best possible model has a C(alpha) RMSD value of 5.3A. Moreover, the resulting model fits better into the cryoEM density map than the initial template structure. The method is being implemented in our program MODELLER for protein structure modeling by satisfaction of spatial restraints and will be applicable to the rapidly increasing number of cryoEM density maps of macromolecular assemblies. %Z 0022-2836 (Print) Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. %U http://salilab.org/pdf/Topf_JMolBiol_2006.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, QB3, 1700 4th Street, Suite 503B, University of California at San Francisco, San Francisco, CA 94143-2552, USA. %0 Journal Article %A Topf, M %A Baker, ML %A John, B %A Chiu, W %A Sali, A %D 2005 %T Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy %B J Struct Biol %V 149 %N 2 %P 191-203 %8 Feb %! Structural characterization of components of protein assemblies by comparative modeling and electron cryo-microscopy. %@ 1047-8477 %M 15681235 %L 143 %F 143 %K Cryoelectron Microscopy Models, Molecular Protein Conformation Sequence Alignment %X We explore structural characterization of protein assemblies by a combination of electron cryo-microscopy (cryoEM) and comparative protein structure modeling. Specifically, our method finds an optimal atomic model of a given assembly subunit and its position within an assembly by fitting alternative comparative models into a cryoEM map. The alternative models are calculated by MODELLER [J. Mol. Biol. 234 (1993) 313] from different sequence alignments between the modeled protein and its template structures. The fitting of these models into a cryoEM density map is performed either by FOLDHUNTER [J. Mol. Biol. 308 (2001) 1033] or by a new density fitting module of MODELLER (Mod-EM). Identification of the most accurate model is based on the correlation between the model accuracy and the quality of fit into the cryoEM density map. To quantify this correlation, we created a benchmark consisting of eight proteins of different structural folds with corresponding density maps simulated at five resolutions from 5 to 15 angstroms, with three noise levels each. Each of the proteins in the set was modeled based on 300 different alignments to their remotely related templates (12-32% sequence identity), spanning the range from entirely inaccurate to essentially accurate alignments. The benchmark revealed that one of the most accurate models can usually be identified by the quality of its fit into the cryoEM density map, even for noisy maps at 15 angstroms resolution. Therefore, a cryoEM density map can be helpful in improving the accuracy of a comparative model. Moreover, a pseudo-atomic model of a component in an assembly may be built better with comparative models of the native subunit sequences than with experimentally determined structures of their homologs. %U http://salilab.org/pdf/Topf_JStructBiol_2005.pdf %+ Department of Biopharmaceutical Sciences, California Institute for Quantitative Biomedical Research, Mission Bay Genentech Hall, 600 16th Street, Suite N472D, University of California, San Francisco, CA 94143, USA. %G eng %0 Journal Article %A Topf, M %A Lasker, K %A Webb, B %A Wolfson, H %A Chiu, W %A Sali, A %D 2008 %T Protein structure fitting and refinement guided by cryo-EM density %B Structure %V 16 %N 2 %P 295-307 %8 Feb %! Protein structure fitting and refinement guided by cryo-EM density. %@ 0969-2126 %2 PMCID2409374 %M 18275820;PMCID:PMC2409374 %L 200 %F 200 %K Cryoelectron Microscopy GroEL Protein Models, Molecular Monte Carlo Method Peptide Elongation Factor Tu Protein Structure, Tertiary %X For many macromolecular assemblies, both a cryo-electron microscopy map and atomic structures of its component proteins are available. Here we describe a method for fitting and refining a component structure within its map at intermediate resolution (<15 A). The atomic positions are optimized with respect to a scoring function that includes the crosscorrelation coefficient between the structure and the map as well as stereochemical and nonbonded interaction terms. A heuristic optimization that relies on a Monte Carlo search, a conjugate-gradients minimization, and simulated annealing molecular dynamics is applied to a series of subdivisions of the structure into progressively smaller rigid bodies. The method was tested on 15 proteins of known structure with 13 simulated maps and 3 experimentally determined maps. At approximately 10 A resolution, Calpha rmsd between the initial and final structures was reduced on average by approximately 53%. The method is automated and can refine both experimental and predicted atomic structures. %Z PMC2409374 %U http://salilab.org/pdf/Topf_Structure_2008.pdf %+ School of Crystallography, Birkbeck College, University of London, Malet Street, London WC1E 7HX, United Kingdom. m.topf@cryst.bbk.ac.uk %G eng %0 Journal Article %A Topf, M %A Sali, A %D 2005 %T Combining electron microscopy and comparative protein structure modeling %B Curr Opin Struct Biol %V 15 %N 5 %P 578-585 %8 Oct %! Combining electron microscopy and comparative protein structure modeling. %@ 0959-440X %M 16118050 %L 155 %F 155 %K Cryoelectron Microscopy Crystallography, X-Ray Models, Molecular Protein Conformation %X Recently, advances have been made in methods and applications that integrate electron microscopy density maps and comparative modeling to produce atomic structures of macromolecular assemblies. Electron microscopy can benefit from comparative modeling through the fitting of comparative models into electron microscopy density maps. Also, comparative modeling can benefit from electron microscopy through the use of intermediate-resolution density maps in fold recognition, template selection and sequence-structure alignment. %U http://salilab.org/pdf/Topf_CurrOpinStructBiol_2005.pdf %+ Department of Biopharmaceutical Sciences, University of California San Francisco, San Francisco, CA 94143, USA. %G eng %0 Journal Article %A Tran, T. H. %A Nguyen, J. V. %A Stecula, A. %A Akutagawa, J. %A Moorman, A. V. %A Braun, B. S. %A Sali, A. %A Mullighan, C. G. %A Shah, N. P. %A Dai, Y. %A Devidas, M. %A Roberts, K. G. %A Smith, C. C. %A Loh, M. L. %D 2021 %T The EBF1-PDGFRB T681I mutation is highly resistant to imatinib and dasatinib in vitro and detectable in clinical samples prior to treatment %B Haematologica %V 106 %6 8 %P 2242-2245 %! The EBF1-PDGFRB T681I mutation is highly resistant to imatinib and dasatinib in vitro and detectable in clinical samples prior to treatment %R 10.3324/haematol.2020.261354 %2 PMCID8327742 %M 33626861 %L 410 %F 410 %U https://salilab.org/pdf/Tran_Haematologica_2021.pdf %0 Journal Article %A Trewhella, J. %A Duff, A. P %A Durand, D. %A Gabel, F. %A Guss, J. M. %A Hendrickson, W. A. %A Hura, G. L. %A Jacques, D. A. %A Kriby, N. M. %A Kwan, A. H. %A Perez, J. %A Pollack, L. %A Ryan, T. M. %A Sali, A. %A Schneidman-Duhovny, D. %A Schwede, T. %A Svergun, D. I. %A Sugiyama, M. %A Tainer, J. A. %A Vachette, P. %A Westbrook, J. %A Whitten, A. E. %D 2017 %T 2017 Publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update %B Acta Cryst D %V D73 %P 710-728 %! 2017 Publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update %R 10.1107/S2059798317011597 %2 PMCID5586245 %M 28876235 %L 375 %F 375 %U https://salilab.org/pdf/Trewhella_ActaCrystD_2017.pdf %0 Journal Article %A Trewhella, J. %A Hendrickson, W. %A Kleywegt, G. %A Sali, A. %A Sato, M. %A Schwede, T. %A Svergun, D. %A Tainer, J. %A Westbrook, J. %A Berman, H. %D 2013 %T Report of the wwPDB Small-Angle Scattering Task Force: Data Requirements for Biomolecular Modeling and the PDB %B Structure %V 21 %P 875-881 %! Report of the wwPDB Small-Angle Scattering Task Force: Data Requirements for Biomolecular Modeling and the PDB %2 PMCID TBD by Journal %M 23747111;PMCID:PMC Journal- In Process %L 303 %F 303 %U http://salilab.org/pdf/Trewhella_Structure_2013.pdf %0 Journal Article %A Trinidad, J. %A Barkan, D. %A Gulledge, B. %A Thalhammer, A. %A Sali, A. %A Schoepfer, R. %A Burlingame, A. %D 2012 %T Global Identification and Characterization of Both O-GlcNAcylation and Phosphorylation at the Murine Synapse %B Mol Cell Proteomics %V 11 %P 215-229 %! Global Identification and Characterization of Both O-GlcNAcylation and Phosphorylation at the Murine Synapse %2 PMCID3412957 %M 22645316;PMCID:PMC3412957 %L 282 %F 282 %U http://salilab.org/pdf/Trinidad_MolCellProteomics_2012.pdf %0 Book Section %A Turk, V. %A Brzin, J. %A Lenarcic, B. %A Sali, A. %A Machleidt, W. %D 1986 %T Human stefins and cystatis: their properties and structural relationships %E Turk, V. %B Cysteine Proteinases and Their Inhibitors; First International Symposium, Portoroz, Yugoslavia, September 15-18, 1985. Xvi+846p. %C Berlin, West Germany; New York, New York, USA. %I Walter De Gruyter and Co. %P 429-442 %! Human stefins and cystatis: their properties and structural relationships %L 3 %F 3 %U http://salilab.org/pdf/Turk_CystProt_1986.pdf %0 Book Section %A Turk, V. %A Jerala, R. %A Lenarcic, B. %A Sali, A. %D 1989 %T Structural and functional aspects of human cathepsins B %E Katunuma, N. %E Kominami, E. %B Intracellular Proteolysis: Mechanisms and Regulations %I Japan Scientific Societies Press %P 27 -37 %! Structural and functional aspects of human cathepsins B %L 7 %F 7 %K Human %Z TY - CHAP %U http://salilab.org/pdf/Turk_IntraProt_1989.pdf %+ Tokyo %0 Journal Article %A Upla, P. %A Kim, S. J. %A Sampathkumar, P. %A Dutta, K. %A Cahill, S. M. %A Chemmama, I. E. %A Williams, R. %A Bonanno, J. B. %A Rice, W. J. %A Stokes, D. L. %A Cowburn, D. %A Almo, S. C. %A Sali, A. %A Rout, M. P. %A Fernandez-Martinez, J. %D 2017 %T Molecular Architecture of the Major Membrane Ring Component of the Nuclear Pore Complex %B Structure %V 25 %N 3 %P 434-445 %7 2017/02/02 %8 Feb %! Molecular Architecture of the Major Membrane Ring Component of the Nuclear Pore Complex %@ 1878-4186 %R 10.1016/j.str.2017.01.006 %2 PMCID5342941 %M 28162953 %L 370 %F 370 %X The membrane ring that equatorially circumscribes the nuclear pore complex (NPC) in the perinuclear lumen of the nuclear envelope is composed largely of Pom152 in yeast and its ortholog Nup210 (or Gp210) in vertebrates. Here, we have used a combination of negative-stain electron microscopy, nuclear magnetic resonance, and small-angle X-ray scattering methods to determine an integrative structure of the ∼120 kDa luminal domain of Pom152. Our structural analysis reveals that the luminal domain is formed by a flexible string-of-pearls arrangement of nine repetitive cadherin-like Ig-like domains, indicating an evolutionary connection between NPCs and the cell adhesion machinery. The 16 copies of Pom152 known to be present in the yeast NPC are long enough to form the observed membrane ring, suggesting how interactions between Pom152 molecules help establish and maintain the NPC architecture. %U https://salilab.org/pdf/Upla_Structure_2017.pdf %G eng %0 Journal Article %A Vallat, B. %A Webb, B.M. %A Westbrook, J.D. %A Goddard, T.D. %A Hanke, C.A. %A Graziadei, A. %A Peisach, E. %A Zalevsky, A. %A Sagendorf, J. %A Tangmunarunkit, H. %A Voinea, S. %A Sekharan, M. %A Yu, J. %A Bonvin, A.A.M.J.J. %A DiMaio, F. %A Hummer, G. %A Meiler, J. %A Tajkhorshid, E. %A Ferrin, T.E. %A Lawson, C.L. %A Leitner, A. %A Rappsilber, J. %A Seidel, C.A.M. %A Jeffries, C.M. %A Burley, S.K. %A Hoch, J.C. %A Kurisu, G. %A Morris, K. %A Patwardhan, A. %A Velankar, S. %A Schwede, T. %A Trewhella, J. %A Kesselman, C. %A Berman, H.M. %A Sali, A. %D 2024 %T IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods %B J Mol Biol %V 436 %N 17 %P 168546 %! IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods %R 10.1016/j.jmb.2024.168546 %M 38508301 %L 454 %F 454 %0 Journal Article %A Vallat, B. %A Webb, B. %A Fayazi, M. %A Voinea, S. %A Tangmunarunkit, H. %A Ganesan, S.J. %A Lawson, C.L. %A Westbrook, J.D. %A Kesselman, C. %A Sali, A. %A Berman, H.M. %D 2021 %T New system for archiving integrative structures %B Acta Cryst D %V 77 %6 12 %P 1486-1496 %! New system for archiving integrative structures %R 10.1107/S2059798321010871 %2 PMCID8647179 %M 34866606 %L 420 %F 420 %U https://salilab.org/pdf/Vallat_ActaCrystD_2021.pdf %0 Journal Article %A Vallat, B %A Tauriello, G %A Bienert, S %A Haas, J %A Webb, BM %A Žídek, A %A Zheng, W %A Peisach, E %A Piehl, DW %A Anischanka, I %A Sillitoe, I %A Tolchard, J %A Varadi, M %A Baker, D %A Orengo, C %A Zhang, Y %A Hoch, JC %A Kurisu, G %A Patwardhan, A %A Velankar, S %A Burley, SK %A Sali, A %A Schwede, T %A Berman, HM %A Westbrook, JD %D 2023 %T ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models %B J Mol Biol %V 435 %N 14 %P 168021 %! ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models %R 10.1016/j.jmb.2023.168021 %2 PMCID10293049 %M 36828268 %L 441 %F 441 %U https://salilab.org/pdf/Vallat_JMolBiol_2023.pdf %0 Journal Article %A Vallat, B %A Webb, B %A Westbrook, J %A Sali, A %A Berman, HM %D 2018 %T Development of a prototype system for archiving integrative/hybrid structure models of biological macromolecules %B Structure %V 26 %N 6 %P 894-904.e2 %! Development of a prototype system for archiving integrative/hybrid structure models of biological macromolecules %R 10.1016/j.str.2018.03.011 %2 PMCID5990459 %M 29657133 %L 385 %F 385 %U https://salilab.org/pdf/Vallat_Structure_2018.pdf %0 Journal Article %A Vallat, B %A Webb, B %A Westbrook, J %A Sali, A %A Berman, HM %D 2019 %T Archiving and Disseminating Integrative Structure Models %B J Biomol NMR %V 73 %N 6-7 %P 385-398 %! Archiving and Disseminating Integrative Structure Models %R 10.1007/s10858-019-00264-2 %2 PMCID6692293 %M 31278630 %L 396 %F 396 %U https://salilab.org/pdf/Vallat_JBiomolNMR_2019.pdf %0 Journal Article %A van Dam, T. %A Townsend, M. %A Turk, M. %A Schlessinger, A. %A Sali, A. %A Field, M. %A Huynen, M. %D 2013 %T Evolution of modular intraflagellar transport from a coatomer-like progenitor %B Proc Natl Acad Sci USA %V 110 %P 6943-6948 %! Evolution of modular intraflagellar transport from a coatomer-like progenitor %2 PMCID3637775 %M 23569277;PMCID:PMC3637775 %L 296 %F 296 %U http://salilab.org/pdf/van Dam_ProcNatlAcadSciUSA_2013.pdf %0 Journal Article %A Veerapandian, B. %A Cooper, J. B. %A Sali, A. %A Blundell, T. L. %D 1990 %T X-ray analyses of aspartic proteinases. III Three-dimensional structure of endothiapepsin complexed with a transition-state isostere inhibitor of renin at 1.6 A resolution %B J Mol Biol %V 216 %N 4 %P 1017-1029 %! X-ray analyses of aspartic proteinases. III Three-dimensional structure of endothiapepsin complexed with a transition-state isostere inhibitor of renin at 1.6 A resolution %@ 0022-2836 %M 2266553 %L 13 %F 13 %U http://salilab.org/pdf/Veerapandian_JMolBiol_1990.pdf %0 Journal Article %A Veerapandian, B. %A Cooper, J. B. %A Sali, A. %A Blundell, T. L. %A Rosati, R. L. %A Dominy, B. W. %A Damon, D. B. %A Hoover, D. J. %D 1992 %T Direct observation by X-ray analysis of the tetrahedral "intermediate" of aspartic proteinases %B Protein Sci %V 1 %N 3 %P 322-328 %8 Mar %! Direct observation by X-ray analysis of the tetrahedral "intermediate" of aspartic proteinases %M 1304340;PMCID:PMC2142209 %L 24 %F 24 %K Amino Acid Sequence Aspartic Acid Aspartic Endopeptidases/*chemistry/metabolism Binding Sites Hydrogen Bonding Models, Molecular Oligopeptides/metabolism Protein Conformation Renin/antagonists & inhibitors X-Ray Diffraction/methods %X We report the X-ray analysis at 2.0 A resolution for crystals of the aspartic proteinase endothiapepsin (EC 3.4.23.6) complexed with a potent difluorostatone-containing tripeptide renin inhibitor (CP-81,282). The scissile bond surrogate, an electrophilic ketone, is hydrated in the complex. The pro-(R) (statine-like) hydroxyl of the tetrahedral carbonyl hydrate is hydrogen-bonded to both active-site aspartates 32 and 215 in the position occupied by a water in the native enzyme. The second hydroxyl oxygen of the hydrate is hydrogen-bonded only to the outer oxygen of Asp 32. These experimental data provide a basis for a model of the tetrahedral intermediate in aspartic proteinase-mediated cleavage of the amide bond. This indicates a mechanism in which Asp 32 is the proton donor and Asp 215 carboxylate polarizes a bound water for nucleophilic attack. The mechanism involves a carboxylate (Asp 32) that is stabilized by extensive hydrogen bonding, rather than an oxyanion derivative of the peptide as in serine proteinase catalysis. %Z 0961-8368 Journal Article %U http://salilab.org/pdf/Veerapandian_ProteinSci_1992.pdf %+ Department of Crystallography, Birkbeck College, London, UK. %0 Journal Article %A Velazquez-Muriel, J.A. %A Lasker, K. %A Russel, D. %A Phillips, J. %A Webb, B. %A Schneidman-Duhovny, D. %A Sali, A. %D 2012 %T Assembly of macromolecular complexes by satisfaction of spatial restraints from electron microscopy images %B Proc Natl Acad Sci USA %V 109 %P 18821-18826 %! Assembly of macromolecular complexes by satisfaction of spatial restraints from electron microscopy images %2 PMCID3503186 %M 23112201;PMCID:PMC3503186 %L 284 %F 284 %U http://salilab.org/pdf/Velazquez_ProcNatlAcadSciUSA_2012.pdf %0 Journal Article %A Vernal, J. %A Fiser, A. %A Sali, A. %A Muller, M. %A Cazzulo, J. J. %A Nowicki, C. %D 2002 %T Probing the specificity of a trypanosomal aromatic alpha-hydroxy acid dehydrogenase by site-directed mutagenesis %B Biochem Biophys Res Commun %V 293 %N 1 %P 633-639 %! Probing the specificity of a trypanosomal aromatic alpha-hydroxy acid dehydrogenase by site-directed mutagenesis %@ 0006-291X %M 12054650 %L 104 %F 104 %X The aromatic L-a-hydroxy acid dehydrogenase (AHDAH) from Trypanosoma cruzi has over 50% sequence identity with cytosolic malate dehydrogenases (cMDHs), yet it is unable to reduce oxaloacetate. Molecular modeling of the three-dimensional structure of AHADH using the pig cMDH as template directed the construction of several mutants. AHADH shares with MDHs the essential catalytic residues H195 and R171 (using Eventoffs numbering). The AHADH A102R mutant became able to reduce oxaloacetate, while remaining fully active towards aromatic alpha-oxoacids. The Y237G mutant diminished its affinity for all of the natural substrates, whereas the double mutant A102R/Y237G was more active than Y237G and had similar activity with oxaloacetate and with aromatic substrates. The present results reinforce our proposal that AHADH arose by a moderate number of point mutations from a cMDH no longer present in the parasite. (C) 2002 Elsevier Science (USA). All rights reserved. %U http://salilab.org/pdf/Vernal_BiochemBiophysResCommun_2002.pdf %0 Journal Article %A Verschueren, E. %A Von Dollen, J. %A Cimermancic, P. %A Gulbahce, N. %A Sali, A. %A Krogan, N. %D 2015 %T Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST. %B Curr Protoc Bioinformatics %V 49 %P 8.19.1-8.19.16 %! Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST. %2 PMCID4378866 %M 25754993;PMCID:PMC4378866 %L 332 %F 332 %U http://salilab.org/pdf/Verschueren_CurrProtocBioinformatics_2015.pdf %0 Journal Article %A Viswanath, S. %A Bonomi, M. %A Kim, S. J. %A Klenchin, V. A. %A Taylor, K. C. %A Yabut, K. C. %A Umbreit, N. T. %A Van Epps, H. A. %A Meehl, J. %A Jones, M. H. %A Russel, D. %A Velazquez-Muriel, J. A. %A Winey, M. %A Rayment, I. %A Davis, T. N. %A Sali, A. %A Muller, E. G. %D 2017 %T The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling %B Mol Biol Cell %V 28 %N 23 %P 3298-3314 %! The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling %R 10.1091/mbc.E17-06-0397 %2 PMCID5687031 %M 28814505 %L 376 %F 376 %U https://salilab.org/pdf/Viswanath_MolBiolCell_2017.pdf %0 Journal Article %A Viswanath, S. %A Chemmama, I. %A Cimermancic, P. %A Sali, A. %D 2017 %T Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures %B Biophys J %V 113 %N 11 %P 2344-2353 %! Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures %R 10.1016/j.bpj.2017.10.005 %2 PMCID5768525 %M 29211988 %L 377 %F 377 %U https://salilab.org/pdf/Viswanath_BiophysJ_2017.pdf %0 Journal Article %A Viswanath, S %A Sali, A %D 2018 %T Optimizing model representation for integrative structure determination of macromolecular assemblies. %B Proc Natl Acad Sci USA %P pii: 201814649 %! Optimizing model representation for integrative structure determination of macromolecular assemblies. %R 10.1073/pnas.1814649116 %2 PMCID6329962 %M 30587581 %L 392 %F 392 %U https://salilab.org/pdf/Viswanath_ProcNatlAcadSciUSA_2018.pdf %0 Journal Article %A Waight, A. %A Pedersen, B. %A Schlessinger, A. %A Bonomi, M. %A Chau, B. %A Roe-Zurz, Z. %A Risenmay, A. %A Sali, A. %A Stroud, R. %D 2013 %T Structural basis for alternating access of a Eukaryotic Calcium/Proton exchanger %B Nature %V 499 %P 107-110 %! Structural basis for alternating access of a Eukaryotic Calcium/Proton exchanger %2 PMCID3702627 %M 23685453;PMCID:PMC3702627 %L 297 %F 297 %U http://salilab.org/pdf/Waight_Nature_2013.pdf %0 Journal Article %A Wang, SX %A Pandey, KC %A Scharfstein, J %A Whisstock, J %A Huang, RK %A Jacobelli, J %A Fletterick, RJ %A Rosenthal, PJ %A Abrahamson, M %A Brinen, LS %A Rossi, A %A Sali, A %A McKerrow, JH %D 2007 %T The structure of chagasin in complex with a cysteine protease clarifies the binding mode and evolution of an inhibitor family %B Structure %V 15 %N 5 %P 535-543 %8 May %! The structure of chagasin in complex with a cysteine protease clarifies the binding mode and evolution of an inhibitor family. %@ 0969-2126 %M 17502099 %L 177 %F 177 %K Amino Acid Sequence Animals Cysteine Endopeptidases Evolution, Molecular Molecular Sequence Data Multigene Family Protease Inhibitors Protein Binding Protozoan Proteins Trypanosoma cruzi %X Protein inhibitors of proteolytic enzymes regulate proteolysis and prevent the pathological effects of excess endogenous or exogenous proteases. Cysteine proteases are a large family of enzymes found throughout the plant and animal kingdoms. Disturbance of the equilibrium between cysteine proteases and natural inhibitors is a key event in the pathogenesis of cancer, rheumatoid arthritis, osteoporosis, and emphysema. A family (I42) of cysteine protease inhibitors (http://merops.sanger.ac.uk) was discovered in protozoan parasites and recently found widely distributed in prokaryotes and eukaryotes. We report the 2.2 A crystal structure of the signature member of the I42 family, chagasin, in complex with a cysteine protease. Chagasin has a unique variant of the immunoglobulin fold with homology to human CD8alpha. Interactions of chagasin with a target protease are reminiscent of the cystatin family inhibitors. Protein inhibitors of cysteine proteases may have evolved more than once on nonhomologous scaffolds. %U http://salilab.org/pdf/Wang_Structure_2007.pdf %+ Department of Pathology, University of California, San Francisco, San Francisco, CA 94143, USA. %G eng %0 Journal Article %A Wang, X %A Chemmama, IE %A Yu, C %A Huszagh, A %A Xu, Y %A Viner, R %A Block, SA %A Cimermancic, P %A Rychnovsky, S %A Ye, Y %A Sali, A %A Huang, L %D 2017 %T The proteasome-interacting Ecm29 protein disassembles the 26S proteasome in response to oxidative stress %B J Biol Chem %V 292 %N 39 %P 16310-16320 %! The proteasome-interacting Ecm29 protein disassembles the 26S proteasome in response to oxidative stress %R 10.1074/jbc.M117.803619 %2 PMCID5625060 %M 28821611 %L 363 %F 363 %U https://salilab.org/pdf/Wang_JBiolChem_2017.pdf %0 Journal Article %A Wang, X %A Cimermancic, P %A Yu, C %A Schweitzer, A %A Chopra, N %A Engel, JL %A Greenberg, C %A Huszagh, AS %A Beck, F %A Sakata, E %A Yang, Y %A Novitsky, EJ %A Leitner, A %A Nanni, P %A Kahraman, A %A Guo, X %A Dixon, JE %A Rychnovsky, SD %A Aebersold, R %A Baumeister, W %A Sali, A %A Huang, L %D 2017 %T Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome %B Mol Cell Proteomics %V 16 %N 5 %P 840-854 %! Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome %R 10.1074/mcp.M116.065326 %2 PMCID5417825 %M 28292943 %L 365 %F 365 %U https://salilab.org/pdf/Wang_MolCellProteomics_2017.pdf %0 Journal Article %A Ward, A. %A Sali, A. %A Wilson, I. %D 2013 %T Integrative structural biology %B Science %V 339 %P 913-915 %! Integrative structural biology %2 PMCID3633482 %M 23430643;PMCID:PMC3633482 %L 300 %F 300 %U http://salilab.org/pdf/Ward_Science_2013.pdf %0 Journal Article %A Watts, J. C. %A Giles, K. %A Saltzberg, D. J. %A Dugger, B. N. %A Patel, S. %A Oehler, A. %A Bhardwaj, S. %A Sali, A. %A Prusiner, S. B. %D 2016 %T Guinea Pig Prion Protein Supports Rapid Propagation of Bovine Spongiform Encephalopathy and Variant Creutzfeldt-Jakob Disease Prions %B J Virol %V 90 %N 21 %P 9558-9569 %! Guinea Pig Prion Protein Supports Rapid Propagation of Bovine Spongiform Encephalopathy and Variant Creutzfeldt-Jakob Disease Prions %R 10.1128/JVI.01106-16 %2 PMCID5068510 %M 27440899 %L 355 %F 355 %U https://salilab.org/pdf/Watts_JVirol_2016.pdf %0 Book Section %A Webb, B. %A Eswar, N. %A Fan, H. %A Khuri, N. %A Pieper, U. %A Dong, G.Q. %A Sali, A. %D 2014 %T Comparative Modeling of Drug Target Proteins %E Reedijk, J. %B Chemistry, Molecular Sciences and Chemical Engineering %C Waltham, MA %I Elsevier %! Comparative Modeling of Drug Target Proteins %L 314 %F 314 %U http://salilab.org/pdf/Webb_ChemMolSciChemEng_2014.pdf %0 Book Section %A Webb, B. %A Lasker, K. %A Schneidman-Duhovny, D. %A Tjioe, E. %A Phillips, J. %A Kim, S.J. %A Velazquez-Muriel, J. %A Russel, D. %A Sali, A. %D 2011 %T Modeling of Proteins and their Assemblies with the Integrative Modeling Platform %B Methods in Molecular Biology %I Humana Press %P 377-397 %! Modeling of Proteins and their Assemblies with the Integrative Modeling Platform %M 21877292;PMCID:PMC Journal- In Process %L 252 %F 252 %U http://salilab.org/pdf/Webb_MethodsMolBiol_2011.pdf %0 Book Section %A Webb, B. %A Lasker, K. %A Velazquez-Muriel, J. %A Schneidman-Duhovny, D. %A Pellarin, R. %A Bonomi, M. %A Greenberg, C. %A Raveh, B. %A Tjioe, E. %A Russel, D. %A Sali, A. %D 2014 %T Modeling of proteins and their assemblies with the Integrative Modeling Platform %E Chen, Y. %B Methods Mol Biol %C London, UK %I Humana Press %V 1091 %P 277-295 %! Modeling of proteins and their assemblies with the Integrative Modeling Platform %M 24203340 %L 319 %F 319 %U http://salilab.org/pdf/Webb_MethodsMolBiol_2014.pdf %0 Book Section %A Webb, B. %A Sali, A. %D 2014 %T Comparative Protein Structure Modeling Using Modeller %B Curr Protoc Bioinformatics %I John Wiley and Sons %V 47 %P 5.6.1-5.6.32 %! Comparative Protein Structure Modeling Using Modeller %M 25199792 %L 331 %F 331 %U https://salilab.org/pdf/Webb_CurrProtBioinform_2014a.pdf %0 Book Section %A Webb, B. %A Sali, A. %D 2014 %T Protein structure modeling with MODELLER %E Kihara, D. %B Methods in Molecular Biology %C New York %I Springer %V 1137 %P 1-15 %! Protein structure modeling with MODELLER %L 315 %F 315 %U http://salilab.org/pdf/Webb_MethodsInMolBiol_2013.pdf %0 Book Section %A Webb, B. %A Sali, A. %D 2016 %T Comparative Protein Structure Modeling Using MODELLER %B Curr Protoc Protein Sci %I John Wiley and Sons %V 86 %P 2.9.1-2.9.37 %! Comparative Protein Structure Modeling Using MODELLER %R 10.1002/cpps.20 %M 27801516 %L 354 %F 354 %U https://salilab.org/pdf/Webb_CurrProtProtSci_2016.pdf %0 Book Section %A Webb, B. %A Sali, A. %D 2016 %T Comparative Protein Structure Modeling Using MODELLER %B Curr Protoc Bioinformatics %I John Wiley and Sons %V 54 %P 5.6.1-5.6.37 %! Comparative Protein Structure Modeling Using MODELLER %R 10.1002/cpbi.3 %M 27322406;PMCID:PMC5031415 %L 362 %F 362 %U https://salilab.org/pdf/Webb_CurrProtBioinform_2016.pdf %0 Journal Article %A Webb, B. %A Sali, A. %D 2021 %T Protein Structure Modeling with MODELLER %B Meth Mol Biol %V 2199 %P 239-255 %! Protein Structure Modeling with MODELLER %@ 1940-6029 %R 10.1007/978-1-0716-0892-0_14 %M 33125654 %L 419 %F 419 %K Databases, Protein Models, Molecular Protein Conformation Proteins Software Comparative modeling Fold assignment Model assessment Multiple templates Sequence-structure alignment %X Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences. %Z Webb, Benjamin Sali, Andrej 2020/10/31 %U https://salilab.org/pdf/Webb_MethodsMolBiol_2021.pdf %+ Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA. California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, CA, USA. Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA. sali@salilab.org. Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA. sali@salilab.org. California Institute for Quantitative Biosciences (QB3), University of California San Francisco, San Francisco, CA, USA. sali@salilab.org. %G eng %0 Journal Article %A Webb, B. %A Viswanath, S. %A Bonomi, M. %A Pellarin, R. %A Greenberg, C. H. %A Saltzberg, D. %A Sali, A. %D 2018 %T Integrative structure modeling with the Integrative Modeling Platform %B Prot Sci %V 27 %P 245-258 %! Integrative structure modeling with the Integrative Modeling Platform %R 10.1002/pro.3311 %2 PMCID5734277 %M 28960548 %L 380 %F 380 %U https://salilab.org/pdf/Webb_ProtSci_2018.pdf %0 Book Section %A Webb, B %A Sali, A %D 2017 %T Protein structure modeling with MODELLER %B Meth Mol Biol %V 1654 %P 39-54 %! Protein structure modeling with MODELLER %M 28986782 %L 369 %F 369 %U https://salilab.org/pdf/Webb_MethodsMolBiol_2017.pdf %0 Journal Article %A Weinkam, P. %A Chen, Y.C. %A Pons, J. %A Sali, A. %D 2013 %T Impact of mutations on the allosteric conformational equilibrium %B J Mol Biol %V 425 %P 647-661 %! Impact of mutations on the allosteric conformational equilibrium %2 PMCID3557769 %M 23228330;PMCID:PMC3557769 %L 299 %F 299 %U http://salilab.org/pdf/Weinkam_JMolBiol_2012a.pdf %0 Journal Article %A Weinkam, P. %A Pons, J. %A Sali, A. %D 2012 %T Structure-based Model of Allostery Predicts Coupling Between Distant Sites %B Proc Natl Acad Sci USA %V 109 %P 4875-4880 %! Structure-based Model of Allostery Predicts Coupling Between Distant Sites %2 PMCID3324024 %M 22403063;PMCID:PMC3324024 %L 271 %F 271 %U http://salilab.org/pdf/Weinkam_ProcNatlAcadSciUSA_2012.pdf %W https://github.com/salilab/allosmod-lib %0 Journal Article %A Weinkam, P. %A Sali, A. %D 2013 %T Mapping Polymerization and Allostery of Hemoglobin S Using Point Mutations %B J Phys Chem B %V 117 %P 13058-13068 %! Mapping Polymerization and Allostery of Hemoglobin S Using Point Mutations %2 PMCID3973026 %M 23957820;PMCID:PMC3973026 %L 307 %F 307 %U http://salilab.org/pdf/Weinkam_JPhysChemB_2013a.pdf %0 Journal Article %A White, K. L. %A Singla, J. %A Loconte, V. %A Chen, J. H. %A Ekman, A. %A Sun, L. %A Zhang, X. %A Francis, J. P. %A Li, A. %A Lin, W. %A Tseng, K. %A McDermott, G. %A Alber, F. %A Sali, A. %A Larabell, C. %A Stevens, R. C. %D 2020 %T Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography %B Sci Adv %V 6 %N 50 %7 2020/12/11 %8 Dec %! Visualizing subcellular rearrangements in intact β cells using soft x-ray tomography %@ 2375-2548 %R 10.1126/sciadv.abc8262 %2 PMCID7725475 %M 33298443 %L 408 %F 408 %X Characterizing relationships between cell structures and functions requires mesoscale mapping of intact cells showing subcellular rearrangements following stimulation; however, current approaches are limited in this regard. Here, we report a unique application of soft x-ray tomography to generate three-dimensional reconstructions of whole pancreatic β cells at different time points following glucose-stimulated insulin secretion. Reconstructions following stimulation showed distinct insulin vesicle distribution patterns reflective of altered vesicle pool sizes as they travel through the secretory pathway. Our results show that glucose stimulation caused rapid changes in biochemical composition and/or density of insulin packing, increased mitochondrial volume, and closer proximity of insulin vesicles to mitochondria. Costimulation with exendin-4 (a glucagon-like peptide-1 receptor agonist) prolonged these effects and increased insulin packaging efficiency and vesicle maturation. This study provides unique perspectives on the coordinated structural reorganization and interactions of organelles that dictate cell responses. %Z 2375-2548 White, Kate L Orcid: 0000-0001-8894-9621 Singla, Jitin Orcid: 0000-0001-6225-0783 Loconte, Valentina Orcid: 0000-0001-5715-9993 Chen, Jian-Hua Orcid: 0000-0002-7998-0878 Ekman, Axel Sun, Liping Zhang, Xianjun Orcid: 0000-0003-3554-780x Francis, John Paul Li, Angdi Orcid: 0000-0001-6143-6295 Lin, Wen Tseng, Kaylee Orcid: 0000-0001-7450-2247 McDermott, Gerry Alber, Frank Orcid: 0000-0003-1981-8390 Sali, Andrej Orcid: 0000-0003-0435-6197 Larabell, Carolyn Orcid: 0000-0002-6262-4789 Stevens, Raymond C Orcid: 0000-0002-4522-8725 Journal Article United States Sci Adv. 2020 Dec 9;6(50):eabc8262. doi: 10.1126/sciadv.abc8262. Print 2020 Dec. %U https://salilab.org/pdf/White_SciAdv_2020.pdf %+ Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. stevens@usc.edu calarabell@lbl.gov sali@salilab.org katewhit@usc.edu. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. Institute for Quantitative and Computational Biosciences, Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA. iHuman Institute, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China. Department of Anatomy, University of California, San Francisco, San Francisco, CA 94143, USA. Department of Computer Science, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA. California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA. stevens@usc.edu calarabell@lbl.gov sali@salilab.org katewhit@usc.edu. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. stevens@usc.edu calarabell@lbl.gov sali@salilab.org katewhit@usc.edu. %G eng %0 Journal Article %A Winn, V. D. %A Haimov-Kochman, R. %A Paquet, A. C. %A Yang, Y. J. %A Madhusudhan, M. S. %A Gormley, M. %A Feng, K. T. %A Bernlohr, D. A. %A McDonagh, S. %A Pereira, L. %A Sali, A. %A Fisher, S. J. %D 2007 %T Gene expression profiling of the human maternal-fetal interface reveals dramatic changes between midgestation and term %B Endocrinology %V 148 %N 3 %P 1059-1079 %8 Mar %! Gene expression profiling of the human maternal-fetal interface reveals dramatic changes between midgestation and term %M 17170095 %L 188 %F 188 %K Female *Gene Expression Profiling Gene Expression Regulation, Developmental Gene Regulatory Networks *Gestational Age Humans *Maternal-Fetal Exchange Models, Biological Placenta/*metabolism Pregnancy/*metabolism Term Birth/*metabolism %X Human placentation entails the remarkable integration of fetal and maternal cells into a single functional unit. In the basal plate region (the maternal-fetal interface) of the placenta, fetal cytotrophoblasts from the placenta invade the uterus and remodel the resident vasculature and avoid maternal immune rejection. Knowing the molecular bases for these unique cell-cell interactions is important for understanding how this specialized region functions during normal pregnancy with implications for tumor biology and transplantation immunology. Therefore, we undertook a global analysis of the gene expression profiles at the maternal-fetal interface. Basal plate biopsy specimens were obtained from 36 placentas (14-40 wk) at the conclusion of normal pregnancies. RNA was isolated, processed, and hybridized to HG-U133A&B Affymetrix GeneChips. Surprisingly, there was little change in gene expression during the 14- to 24-wk interval. In contrast, 418 genes were differentially expressed at term (37-40 wk) as compared with midgestation (14-24 wk). Subsequent analyses using quantitative PCR and immunolocalization approaches validated a portion of these results. Many of the differentially expressed genes are known in other contexts to be involved in differentiation, motility, transcription, immunity, angiogenesis, extracellular matrix dissolution, or lipid metabolism. One sixth were nonannotated or encoded hypothetical proteins. Modeling based on structural homology revealed potential functions for 31 of these proteins. These data provide a reference set for understanding the molecular components of the dialogue taking place between maternal and fetal cells in the basal plate as well as for future comparisons of alterations in this region that occur in obstetric complications. %Z 0013-7227 (Print) Journal Article Research Support, N.I.H., Extramural %U http://salilab.org/pdf/Winn_Endocrinology_2007.pdf %+ Reproductive Science, University of Colorado Health Sciences Center, 12800 East 19th Avenue, P.O. Box 6511, Aurora, CO 80045, USA. virginia.winn@uchsc.edu %0 Journal Article %A Winter, M.B. %A La Greca, F. %A Arastu-Kapur, S. %A Cimermancic, P. %A Buchholz, T.J. %A Caiazza, F. %A Anderl, J.L. %A Ravalin, M. %A Bohn, M.F. %A Sali, A. %A O’Donoghue, A.J. %A Craik, C.S. %D 2017 %T Immunoproteasome Functions Explained by Divergence in Cleavage Specificity and Regulation %B eLife %V 6 %P e27364 %! Immunoproteasome Functions Explained by Divergence in Cleavage Specificity and Regulation %R 10.7554/eLife.27364 %2 PMCID5705213 %M 29182146 %L 378 %F 378 %U https://salilab.org/pdf/Winter_eLife_2017.pdf %0 Journal Article %A Wittwer, M.B. %A Zur, A.A. %A Khuri, N. %A Kido, Y. %A Kosaka, A. %A Zhang, X. %A Morrissey, K.M. %A Sali, A. %A Huang, Y. %A Giacomini, K.M. %D 2013 %T Discovery of potent, selective multidrug and toxin extrusion Transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug profiling and computational modeling %B J Med Chem %V 56 %P 781-795 %! Discovery of potent, selective multidrug and toxin extrusion Transporter 1 (MATE1, SLC47A1) inhibitors through prescription drug profiling and computational modeling %2 PMCID4068829 %M 23241029;PMCID:PMC4068829 %L 290 %F 290 %U http://salilab.org/pdf/Wittwer_JMedChem_2012.pdf %0 Journal Article %A Wolf, E. %A Vassilev, A. %A Makino, Y. %A Sali, A. %A Nakatani, Y. %A Burley, S. K. %D 1998 %T Crystal structure of a GCN5-related N-acetyltransferase: Serratia marcescens aminoglycoside 3-N-acetyltransferase %B Cell %V 94 %N 4 %P 439-449 %8 Aug 21 %! Crystal structure of a GCN5-related N-acetyltransferase: Serratia marcescens aminoglycoside 3-N-acetyltransferase %M 9727487 %L 66 %F 66 %K Acetyltransferases/*chemistry/metabolism Amino Acid Sequence Aminoglycosides/metabolism Arylamine N-Acetyltransferase/chemistry Binding Sites Coenzyme A/*chemistry/metabolism Comparative Study Conserved Sequence Crystallography, X-Ray *DNA-Binding Proteins Drug Resistance, Microbial Fungal Proteins/chemistry Hydrogen Bonding Models, Molecular Molecular Sequence Data Multigene Family Protein Kinases/chemistry Protein Structure, Secondary Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. *Saccharomyces cerevisiae Proteins Sequence Homology, Amino Acid Serratia marcescens/*enzymology %X The X-ray structure of a canonical GCN5-related N-acetyltransferase (GNAT), Serratia marcescens aminoglycoside 3-N-acetyltransferase, bound to coenzyme A (CoA) has been determined at 2.3 A resolution. The single domain alpha/beta protein resembles a cupped right hand wrapped around a cylinder and consists of a highly curved, six-stranded beta sheet of mixed polarity that is sandwiched between four alpha helices. The structure includes all four conserved GNAT motifs (C, D, A, and B) and represents the catalytic core of this large enzyme superfamily. Acetyl CoA recognition is mediated by a betaalpha structure derived from GNAT motif A, which presents an invariant Arg/Gln-X-X-Gly-X-Gly/Ala segment for hydrogen bonding with the cofactor. Motif B contributes acidic residues to the binding site for the positively charged antibiotic substrate. %Z 0092-8674 Journal Article %U http://salilab.org/pdf/Wolf_Cell_1998.pdf %+ Laboratories of Molecular Biophysics, The Rockefeller University, New York, New York 10021, USA. %0 Journal Article %A Wong, G. W. %A Li, L. %A Madhusudhan, M. S. %A Krilis, S. A. %A Gurish, M. F. %A Rothenberg, M. E. %A Sali, A. %A Stevens, R. L. %D 2001 %T Tryptase 4, a new member of the chromosome 17 family of mouse serine proteases %B J Biol Chem %V 276 %N 23 %P 20648-20658 %8 Jun 8 %! Tryptase 4, a new member of the chromosome 17 family of mouse serine proteases %M 11259427 %L 94 %F 94 %K Amino Acid Sequence Animals Base Sequence *Chromosome Mapping Cloning, Molecular DNA, Complementary Exons In Situ Hybridization, Fluorescence Introns Mice Models, Molecular Molecular Sequence Data Phylogeny Polymerase Chain Reaction Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid Serine Endopeptidases/chemistry/*genetics/isolation & purification %X Genomic blot analysis raised the possibility that uncharacterized tryptase genes reside on chromosome 17 at the complex containing the three genes that encode mouse mast cell protease (mMCP) 6, mMCP-7, and transmembrane tryptase (mTMT). Probing of GenBank's expressed sequence tag data base with these three tryptase cDNAs resulted in the identification of an expressed sequence tag that encodes a portion of a novel mouse serine protease (now designated mouse tryptase 4 (mT4) because it is the fourth member of this family). 5'- and 3'-rapid amplification of cDNA ends approaches were carried out to deduce the nucleotide sequence of the full-length mT4 transcript. This information was then used to clone its approximately 5.0-kilobase pair gene. Chromosome mapping analysis of its gene, sequence analysis of its transcript, and comparative protein structure modeling of its translated product revealed that mT4 is a new member of the chromosome 17 family of mouse tryptases. mT4 is 40-44% identical to mMCP-6, mMCP-7, and mTMT, and this new serine protease has all of the structural features of a functional tryptase. Moreover, mT4 is enzymatically active when expressed in insect cells. Due to its 17-mer hydrophobic domain at its C terminus, mT4 is a membrane-anchored tryptase more analogous to mTMT than the other members of its family. As assessed by RNA blot, reverse transcriptase-polymerase chain reaction, and/or in situ hybridization analysis, mT4 is expressed in interleukin-5-dependent mouse eosinophils, as well as in ovaries and testes. The observation that recombinant mT4 is preferentially retained in the endoplasmic reticulum of transiently transfected COS-7 cells suggests a convertase-like role for this integral membrane serine protease. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Wong_JBiolChem_2001.pdf %+ Department of Medicine, Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA. %0 Journal Article %A Wong, G. W. %A Tang, Y. %A Feyfant, E. %A Sali, A. %A Li, L. %A Li, Y. %A Huang, C. %A Friend, D. S. %A Krilis, S. A. %A Stevens, R. L. %D 1999 %T Identification of a new member of the tryptase family of mouse and human mast cell proteases which possesses a novel COOH-terminal hydrophobic extension %B J Biol Chem %V 274 %N 43 %P 30784-30793 %8 Oct 22 %! Identification of a new member of the tryptase family of mouse and human mast cell proteases which possesses a novel COOH-terminal hydrophobic extension %M 10521469 %L 74 %F 74 %K Amino Acid Sequence Animals Base Sequence Cell Membrane/enzymology *Chromosome Mapping Cloning, Molecular Female Genome, Human Humans Kinetics Male Mast Cells/*enzymology Mice Mice, Inbred BALB C Mice, Inbred C57BL Molecular Sequence Data Organ Specificity Protein Biosynthesis Recombinant Proteins/chemistry Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. Research Support, U.S. Gov't, P.H.S. Restriction Mapping Sequence Alignment Sequence Homology, Amino Acid Serine Endopeptidases/chemistry/*genetics *Transcription, Genetic %X Mapping of the tryptase locus on chromosome 17 revealed a novel gene 2.3 kilobase 3' of the mouse mast cell protease (mMCP) 6 gene. This 3.7-kilobase gene encodes the first example of a protease in the tryptase family that contains a membrane-spanning segment located at its COOH terminus. Comparative structural studies indicated that the putative transmembrane tryptase (TMT) possesses a unique substrate-binding cleft. As assessed by RNA blot analyses, mTMT is expressed in mice in both strain- and tissue-dependent manners. Thus, different transcriptional and/or post-transcriptional mechanisms are used to control the expression of mTMT in vivo. Analysis of the corresponding tryptase locus in the human genome resulted in the isolation and characterization of the hTMT gene. The hTMT transcript is expressed in numerous tissues and is also translated. Analysis of the tryptase family of genes in mice and humans now indicates that a primordial serine protease gene duplicated early and often during the evolution of mammals to generate a panel of homologous tryptases in each species that differ in their tissue expression, substrate specificities, and physical properties. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Wong_JBiolChem_1999.pdf %+ Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA. %0 Journal Article %A Wong, G. W. %A Yasuda, S. %A Madhusudhan, M. S. %A Li, L. %A Yang, Y. %A Krilis, S. A. %A Sali, A. %A Stevens, R. L. %D 2001 %T Human tryptase epsilon (PRSS22), a new member of the chromosome 16p13.3 family of human serine proteases expressed in airway epithelial cells %B J Biol Chem %V 276 %N 52 %P 49169-49182 %8 Dec 28 %! Human tryptase epsilon (PRSS22), a new member of the chromosome 16p13.3 family of human serine proteases expressed in airway epithelial cells %M 11602603 %L 100 %F 100 %K Adult Amino Acid Sequence Animals Base Sequence Chromosomes, Human, Pair 16/genetics Cloning, Molecular Epithelial Cells/*enzymology/physiology Gene Expression Regulation, Developmental/*physiology Humans Isoenzymes Lung/anatomy & histology/embryology/physiology Models, Molecular Molecular Sequence Data Phylogeny Protein Structure, Tertiary Recombinant Proteins/genetics/metabolism Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Respiratory Mucosa/cytology/*enzymology/physiology Sequence Alignment Serine Endopeptidases/chemistry/classification/genetics/*metabolism Substrate Specificity Tissue Distribution %X Probing of the GenBank expressed sequence tag (EST) data base with varied human tryptase cDNAs identified two truncated ESTs that subsequently were found to encode overlapping portions of a novel human serine protease (designated tryptase epsilon or protease, serine S1 family member 22 (PRSS22)). The tryptase epsilon gene resides on chromosome 16p13.3 within a 2.5-Mb complex of serine protease genes. Although at least 7 of the 14 genes in this complex encode enzymatically active proteases, only one tryptase epsilon-like gene was identified. The trachea and esophagus were found to contain the highest steady-state levels of the tryptase epsilon transcript in adult humans. Although the tryptase epsilon transcript was scarce in adult human lung, it was present in abundance in fetal lung. Thus, the tryptase epsilon gene is expressed in the airways in a developmentally regulated manner that is different from that of other human tryptase genes. At the cellular level, tryptase epsilon is a major product of normal pulmonary epithelial cells, as well as varied transformed epithelial cell lines. Enzymatically active tryptase epsilon is also constitutively secreted from these cells. The amino acid sequence of human tryptase epsilon is 38-44% identical to those of human tryptase alpha, tryptase beta I, tryptase beta II, tryptase beta III, transmembrane tryptase/tryptase gamma, marapsin, and Esp-1/testisin. Nevertheless, comparative protein structure modeling and functional studies using recombinant material revealed that tryptase epsilon has a substrate preference distinct from that of its other family members. These data indicate that the products of the chromosome 16p13.3 complex of tryptase genes evolved to carry out varied functions in humans. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Wong_JBiolChem_2001a.pdf %+ Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA. %0 Journal Article %A Wu, G. %A Fiser, A. %A ter Kuile, B. %A Sali, A. %A Muller, M. %D 1999 %T Convergent evolution of Trichomonas vaginalis lactate dehydrogenase from malate dehydrogenase %B Proc Natl Acad Sci U S A %V 96 %N 11 %P 6285-6290 %! Convergent evolution of Trichomonas vaginalis lactate dehydrogenase from malate dehydrogenase %@ 0027-8424 %M 10339579 %L 71 %F 71 %X Lactate dehydrogenase (LDH) is present in the amitochondriate parasitic protist Trichomonas vaginalis and some but not ail other trichomonad species. The derived amino acid sequence of T. vaginalis LDH (TvLDH) was found to be more closely related to the cytosolic malate dehydrogenase (MDH) of the same species than to any other LDH. A key difference between the two T. vaginalis sequences was that Arg91 of MDH, known to be important in coordinating the C-4 carboxyl of oxalacetate/malate, was replaced by Leu91 in LDH. The change Leu91Arg by site-directed mutagenesis converted TvLDH into an MDH. The reverse single amino acid change Arg91Leu in TvMDH, however, gave a product with no measurable LDH activity. Phylogenetic reconstructions indicate that TvLDH arose from an MDH relatively recently. %U http://salilab.org/pdf/Wu_ProcNatlAcadSciUSA_1999.pdf %0 Journal Article %A Wu, G. %A McArthur, A. G. %A Fiser, A. %A Sali, A. %A Sogin, M. L. %A Muller, M. %D 2000 %T Core histones of the amitochondriate protist, Giardia lamblia %B Mol Biol Evol %V 17 %N 8 %P 1156-1163 %! Core histones of the amitochondriate protist, Giardia lamblia %@ 0737-4038 %M 10908635 %L 84 %F 84 %X Genes coding for the core histones H2a, H2b, H3, and H4 of Giardia lamblia were sequenced. A conserved organism- and gene-specific element, GRGCGCAGATTTVGG, was found upstream of the coding region in all core histone genes. The derived amino acid sequences of all four histones were similar to their homologs in other eukaryotes, although they were among the most divergent members of this protein family. Comparative protein structure modeling combined with energy evaluation of the resulting models indicated that the G. lamblia core histones individually and together can assume the same three-dimensional structures that were established by Xray crystallography for Xenopus laevis histones and the nucleosome core particle. Since G, lamblia represents one of the earliest-diverging eukaryotes in many different molecular trees, the structure of its histones is potentially of relevance to understanding histone evolution. The G. lamblia proteins do not represent an intermediate stage between archaeal and eukaryotic histones. %U http://salilab.org/pdf/Wu_MolBiolEvol_2000.pdf %0 Journal Article %A Wu, H. %A Saltzberg, D. J. %A Kratochvil, H. T. %A Jo, H. %A Sali, A. %A DeGrado, W. F. %D 2019 %T Glutamine Side Chain 13C═18O as a Nonperturbative IR Probe of Amyloid Fibril Hydration and Assembly %B J Am Chem Soc %V 141 %N 18 %P 7320-7326 %7 2019/04/24 %8 May %! Glutamine Side Chain 13C═18O as a Nonperturbative IR Probe of Amyloid Fibril Hydration and Assembly %@ 1520-5126 %R 10.1021/jacs.9b00577 %2 PMCID6800148 %M 30998340 %L 397 %F 397 %X Infrared (IR) spectroscopy has provided considerable insight into the structures, dynamics, and formation mechanisms of amyloid fibrils. IR probes, such as main chain %U https://salilab.org/pdf/Wu_JAmChemSoc_2019.pdf %G eng %0 Journal Article %A Wu, S. %A de Lencastre, H. %A Sali, A. %A Tomasz, A. %D 1996 %T A phosphoglucomutase-like gene essential for the optimal expression of methicillin resistance in Staphylococcus aureus: molecular cloning and DNA sequencing %B Microbial Drug Resistance %V 2 %N 2 %P 277-286 %8 Summer %! A phosphoglucomutase-like gene essential for the optimal expression of methicillin resistance in Staphylococcus aureus: molecular cloning and DNA sequencing %M 9158773 %L 51 %F 51 %K Amino Acid Sequence Cloning, Molecular Culture Media DNA, Bacterial/analysis Electroporation Genes, Bacterial/*genetics Genetic Complementation Test Methicillin Resistance/*genetics/physiology Molecular Sequence Data Mutagenesis, Insertional Phosphoglucomutase/*genetics/metabolism Staphylococcus aureus/enzymology/*genetics %X We describe here the cloning and sequencing of a new auxiliary gene identified by Tn551 insertional mutagenesis of the highly and homogeneously methicillin-resistant Staphylococcus aureus strain COL. The insertionally inactivated mutant RUSA315 had intact mecA and normal amounts of PBP2A, but drastically reduced antibiotic resistance (drop in methicillin MIC from 1600 to 1.5 micrograms ml-1), a unique heterogeneous phenotype, and a compositional change in the cell wall characterized by the complete disappearance of the unsubstituted disaccharide pentapeptide from the peptidoglycan. Cloning in E. coli followed by sequencing located the Tn551 insert omega 720 in an open reading frame of 451 codons, provisionally called femR315, defining a polypeptide with a deduced amino acid sequence that showed over 26% sequence identity and 57% overall sequence similarity with the phosphoglucomutase (PGM) gene of E. coli. The Tn551 insertion site of a previously described mutant 12F (femD) also lies in the same gene as femR315. The wild-type form of femR315 subcloned in a shuttle vector fully restored expression of high level (parental) methicillin resistance in mutant RUSA315. The exact biochemical function of femR315 is not known. However, enzymes similar to PGM catalyze the isomerization of hexose and hexosamine phosphates leading to the formation of glucosamine-1-P, which is an obligate precursor in the biosynthesis of UDP-N-acetylglucosamine (UDP-NAGA). We propose that the suppression of methicillin resistance in RUSA315 is related to some functional or quantitative abnormality of UDP-NAGA metabolism. %Z 1076-6294 Journal Article %U http://salilab.org/pdf/Wu_MicrobialDrugResistance_1996.pdf %+ Laboratory of Microbiology, Rockefeller University, New York, New York 10021-6399, USA. %0 Journal Article %A Wu, X. D. %A Knudsen, B. %A Feller, S. M. %A Zheng, J. %A Sali, A. %A Cowburn, D. %A Hanafusa, H. %A Kuriyan, J. %D 1995 %T Structural basis for the specific interaction of lysine-containing proline-rich peptides with the amino-terminal SH3 domain of c-Crk %B Structure %V 3 %N 2 %P 215-226 %! Structural basis for the specific interaction of lysine-containing proline-rich peptides with the amino-terminal SH3 domain of c-Crk %@ 0969-2126 %M 7735837 %L 39 %F 39 %X Background: Proline-rich segments in the guanine nucleotide exchange factor C3G bind much more strongly to the N-terminal Src homology 3 domain (SH3-N) of the proto-oncogene product c-Crk than to other SH3 domains. The presence of a lysine instead of an arginine in the peptides derived from C3G appears to be crucial for this specificity towards c-Crk. Results: In order to understand the chemical basis of this specificity we have determined the crystal structure of Crk SH3-N in complex with a high affinity peptide from C3G (PPPALPPKKR, K-d similar to 2 mu M) at 1.5 Angstrom resolution. The peptide adopts a polyproline type II helix that binds, as dictated by electrostatic complementarity, in reversed orientation relative to orientation seen in earliest structures of SH3-peptide complexes. A lysine in the C3G peptide is tightly coordinated by three acidic residues in the SH3 domain. In contrast, the co-crystal structure of c-Crk SH3-N and a peptide containing an arginine at the equivalent position (determined at 1.9 Angstrom resolution) reveals non-optimal geometry for the arginine and increased disorder. Conclusions: The c-Crk SH3 domain engages in an unusual lysine-specific interaction that is rarely seen in protein structures, and which appears to be a key determinant of its unique ability to bind the C3G peptides with high affinity. %U http://salilab.org/pdf/Wu_Structure_1995.pdf %0 Journal Article %A Xu, L. Z. %A Sanchez, R. %A Sali, A. %A Heintz, N. %D 1996 %T Ligand specificity of brain lipid-binding protein %B J Biol Chem %V 271 %N 40 %P 24711-24719 %8 Oct 4 %! Ligand specificity of brain lipid-binding protein %M 8798739 %L 49 %F 49 %K Amino Acid Sequence Carrier Proteins/chemistry/genetics/isolation & purification/*metabolism Cell Line Cloning, Molecular Docosahexaenoic Acids/*metabolism Ligands Models, Molecular Molecular Sequence Data Myelin P2 Protein/chemistry/genetics/metabolism *Neoplasm Proteins Nerve Tissue Proteins/genetics/isolation & purification/*metabolism Protein Binding Protein Conformation Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S. Sequence Homology, Amino Acid %X Brain lipid-binding protein (BLBP) is a member of the fatty acid-binding protein (FABP) family. Although BLBP expression in the developing central nervous system is complex, a close correlation between its expression and radial glial differentiation has been observed. Furthermore, antibodies to BLBP can block glial cell differentiation in mixed primary cell cultures. Here we describe the ligand binding properties of BLBP. The binding affinities of BLBP for oleic acid (Kd approximately 0.44 microM) and arachidonic acid (Kd approximately 0.25 microM) are similar to those reported for other FABPs, but BLBP does not bind to palmitic acid or arachidinic acid. These and other experiments establish that BLBP has a strong preference for binding long chain polyunsaturated fatty acids. A probable in vivo ligand for BLBP is docosahexaenoic acid (DHA), since its binding affinity (Kd approximately 10 nM) is the highest yet reported for an FABP/ligand interaction, exceeding even the affinity of retinoic acid for its binding proteins. Furthermore, the requirement of DHA for nervous system development and the coincident expression of BLBP during these developmental stages suggest that the physiologic role of BLBP may involve DHA utilization. Finally, we present a structural model of BLBP/DHA interaction that provides insight into both the structural characteristics important for ligand binding and the effects of specific mutations upon BLBP/ligand interactions. %Z 0021-9258 Journal Article %U http://salilab.org/pdf/Xu_JBiolChem_1996.pdf %+ Howard Hughes Medical Institute, Laboratory of Molecular Biology, The Rockefeller University, New York, New York 10021-6399, USA. %0 Journal Article %A Yang, Y. %A Li, L. X. %A Wong, G. W. %A Krilis, S. A. %A Madhusudhan, M. S. %A Sali, A. %A Stevens, R. L. %D 2002 %T RasGRP4, a new mast cell-restricted Ras guanine nucleotide-releasing protein with calcium- and diacylglycerol-binding motifs - Identification of defective variants of this signaling protein in asthma, mastocytosis, and mast cell leukemia patients and demonstration of the importance of RasGRP4 in mast cell development and function %B J Biol Chem %V 277 %N 28 %P 25756-25774 %! RasGRP4, a new mast cell-restricted Ras guanine nucleotide-releasing protein with calcium- and diacylglycerol-binding motifs - Identification of defective variants of this signaling protein in asthma, mastocytosis, and mast cell leukemia patients and demonstration of the importance of RasGRP4 in mast cell development and function %@ 0021-9258 %R 10.1074/jbc.M202575200 %M 11956218 %L 113 %F 113 %X A cDNA was isolated from interleukin 3-developed, mouse bone marrow-derived mast cells (MCs) that contained an insert (designated mRasGRP4) that had not been identified in any species at the gene, mRNA, or protein level. By using a homology-based cloning approach, the similar to2.6-kb hRasGRP4 transcript was also isolated from the mononuclear progenitors; residing in the peripheral blood of normal individuals. This transcript information was then used to locate the RasGRP4 gene in the mouse and human genomes, to deduce its exon/intron organization, and then to identify 10 single nucleotide polymorphisms in the human gene that result in 5 amino acid differences. The > 15-kb hRasGRP4 gene consists of 18 exons and resides on a regdon of chromosome 19q13.1 that had not been sequenced by the Human Genome Project. Human and mouse MCs and their progenitors selectively express RasGRP4, and this new intracellular protein contains all of the domains present in the RasGRP family of guanine nucleotide exchange factors even though it is <50% identical to its closest homolog. Recombinant RasGRP4 can activate H-Ras in a cation-dependent manner. Transfection experiments also suggest that RasGRP4 is a diacylglycerollphorbol ester receptor. Transcript analysis of an asthma patient, a mastocytosis patient, and the HMC-1 cell line derived from a MC leukemia patient revealed the presence of substantial amounts of non-functional forms of hRasGRP4 due to an inability to remove intron 5 in the precursor transcript. Because only abnormal forms of hRasGRP4 were identified in the HMC-1 cell line, this immature MC progenitor was used to address the function of RasGRP4 in MCs. HMC-1 leukemia cells differentiated and underwent granule maturation when induced to express a normal form of RasGRP4. Thus, RasGRP4 plays an important role in the final stages of MC development. %U http://salilab.org/pdf/Yang_JBiolChem_2002.pdf %0 Journal Article %A Yang, Z. %A Lasker, K. %A Schneidman-Duhovny, D. %A Webb, B. %A Huang, C. %A Pettersen, E. %A Goddard, T. %A Meng, E. %A Sali, A. %A Ferrin, T. %D 2012 %T UCSF Chimera, MODELLER, and IMP: an integrated Modeling System %B J Struct Biol %V 179 %P 269-278 %! UCSF Chimera, MODELLER, and IMP: an integrated Modeling System %2 PMCID3410985 %M 21963794;PMCID:PMC3410985 %L 266 %F 266 %U http://salilab.org/pdf/Yang_JStructBiol_2011.pdf %0 Journal Article %A Yasuda, S. %A Morokawa, N. %A Wong, G. W. %A Rossi, A. %A Madhusudhan, M. S. %A Sali, A. %A Askew, Y. S. %A Adachi, R. %A Silverman, G. A. %A Krilis, S. A. %A Stevens, R. L. %D 2005 %T Urokinase-type plasminogen activator is a preferred substrate of the human epithelium serine protease tryptase epsilon/PRSS22 %B Blood %V 105 %N 10 %P 3893-3901 %8 May 15 %! Urokinase-type plasminogen activator is a preferred substrate of the human epithelium serine protease tryptase epsilon/PRSS22 %M 15701722 %L 149 %F 149 %X Tryptase epsilon is a member of the chromosome 16p13.3 family of human serine proteases that is preferentially expressed by epithelial cells. Recombinant pro-tryptase epsilon was generated to understand how the exocytosed zymogen might be activated outside of the epithelial cell, as well as to address its possible role in normal and diseased states. Using expression/site-directed mutagenesis approaches, we now show that Lys20, Cys90, and Asp92 in the protease's substrate-binding cleft regulate its enzymatic activity. We also show that Arg(-1) in the propeptide domain controls its ability to autoactivate. In vitro studies revealed that recombinant tryptase epsilon possesses a restricted substrate specificity. Once activated, tryptase epsilon cannot be inhibited effectively by the diverse array of protease inhibitors present in normal human plasma. Moreover, this epithelium protease is not highly susceptible to alpha1-antitrypsin or secretory leukocyte protease inhibitor, which are present in the lung. Recombinant tryptase epsilon could not cleave fibronectin, vitronectin, laminin, single-chain tissue-type plasminogen activator, plasminogen, or any prominent serum protein. Nevertheless, tryptase epsilon readily converted single-chain pro-urokinase-type plasminogen activator (pro-uPA/scuPA) into its mature, enzymatically active protease. Tryptase epsilon also was able to induce pro-uPA-expressing smooth muscle cells to increase their migration through a basement membrane-like extracellular matrix. The ability to activate uPA in the presence of varied protease inhibitors suggests that tryptase epsilon plays a prominent role in fibrinolysis and other uPA-dependent reactions in the lung. %Z 0006-4971 Journal Article %U http://salilab.org/pdf/Yasuda_Blood_2005.pdf %+ Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. %0 Journal Article %A Yoshizawa, T. %A Ali, R. %A Fung, H. Y. J. %A Burke, K. A. %A Lin, Y. %A Kim, S. J. %A Jiou, J. %A Soniat, M. %A Oldenbourg, R. %A Sali, A. %A Fawzi, N. L. %A Rosen, M. K. %A Chook, Y. M. %D 2018 %T Nuclear import receptor inhibits phase separation of FUS through binding to multiple sites %B Cell %V 173 %N 3 %P 693-705 %! Nuclear import receptor inhibits phase separation of FUS through binding to multiple sites %R 10.1016/j.cell.2018.03.003 %2 PMCID6234985 %M 29677513 %L 381 %F 381 %U https://salilab.org/pdf/Yoshizawa_Cell_2018.pdf %0 Journal Article %A Yu, Y. %A Li, S. %A Ser, Z. %A Sanyal, T. %A Choi, K. %A Wan, B. %A Kuang, H. %A Sali, A. %A Kentsis, A. %A Patel, D.J. %A Zhao, X. %D 2021 %T Integrative analysis reveals unique structural and functional features of the Smc5/6 complex %B Proc. Natl. Acad. Sci. %V 118 %6 19 %P e2026844118 %! Integrative analysis reveals unique structural and functional features of the Smc5/6 complex %R 10.1073/pnas.2026844118 %2 PMCID8126833 %M 33941673 %L 414 %F 414 %U https://salilab.org/pdf/Yu_ProcNatlAcadSciUSA_2021.pdf %0 Journal Article %A Zeng-Elmore, X. %A Gao, X. %A Pellarin, R. %A Schneidman-Duhovny, D. %A Zhang, X. %A Kozacka, K. %A Tang, Y. %A Sali, A. %A Chalkley, R. %A Cote, R. %A Chu, F. %D 2014 %T Molecular architecture of photoreceptor phosphodiesterase elucidated by chemical cross-linking and integrative modeling %B J Mol Biol %V 426 %P 3713-3728 %! Molecular architecture of photoreceptor phosphodiesterase elucidated by chemical cross-linking and integrative modeling %2 PMCID4253074 %M 25149264;PMCID:PMC4253074 %L 324 %F 324 %U http://salilab.org/pdf/Zeng-Elmore_JMolBiol_2014.pdf %0 Journal Article %A Zhou, C. Y. %A Stoddard, C. I. %A Johnston, J. B. %A Trnka, M. J. %A Echeverria, I. %A Palovcak, E. %A Sali, A. %A Burlingame, A. L. %A Cheng, Y. %A Narlikar, G. J. %D 2017 %T Regulation of Rvb1/Rvb2 by a Domain within the INO80 Chromatin Remodeling Complex Implicates the Yeast Rvbs as Protein Assembly Chaperones %B Cell Rep %V 19 %N 10 %P 2033-2044 %8 Jun %! Regulation of Rvb1/Rvb2 by a Domain within the INO80 Chromatin Remodeling Complex Implicates the Yeast Rvbs as Protein Assembly Chaperones %@ 2211-1247 %R 10.1016/j.celrep.2017.05.029 %2 PMCID5564220 %M 28591576 %L 373 %F 373 %X The hexameric AAA+ ATPases Rvb1 and Rvb2 (Rvbs) are essential for diverse processes ranging from metabolic signaling to chromatin remodeling, but their functions are unknown. While originally thought to act as helicases, recent proposals suggest that Rvbs act as protein assembly chaperones. However, experimental evidence for chaperone-like behavior is lacking. Here, we identify a potent protein activator of the Rvbs, a domain in the Ino80 ATPase subunit of the INO80 chromatin-remodeling complex, termed Ino80INS. Ino80INS stimulates Rvbs' ATPase activity by 16-fold while concomitantly promoting their dodecamerization. Using mass spectrometry, cryo-EM, and integrative modeling, we find that Ino80INS binds asymmetrically along the dodecamerization interface, resulting in a conformationally flexible dodecamer that collapses into hexamers upon ATP addition. Our results demonstrate the chaperone-like potential of Rvb1/Rvb2 and suggest a model where binding of multiple clients such as Ino80 stimulates ATP-driven cycling between hexamers and dodecamers, providing iterative opportunities for correct subunit assembly. %U https://salilab.org/pdf/Zhou_CellRep_2017.pdf %G eng %0 Journal Article %A Zhu, ZY %A Sali, A %A Blundell, TL %D 1992 %T A variable gap penalty function and feature weights for protein 3-D structure comparisons %B Protein Eng %V 5 %N 1 %P 43-51 %8 Jan %! A variable gap penalty function and feature weights for protein 3-D structure comparisons. %@ 0269-2139 %M 1631045 %L 25 %F 25 %K Amino Acid Sequence Aspartic Endopeptidases Azurin Databases, Factual Molecular Sequence Data Plastocyanin Protein Conformation Sequence Alignment Sequence Homology, Nucleic Acid Software %X We have developed a variable gap penalty function for use in the comparison program COMPARER which aligns protein sequences on the basis of their 3-D structures. For deletions and insertions, components are a function of structural features of individual amino acid residues (e.g. secondary structure and accessibility). We have also obtained relative weights for different features used in the comparison by examining the equivalent residues in weight matrices and in alignments for pairs of 3-D structures where the equivalencies are relatively unambiguous. We have used the new parameters and the variable gap penalty function in COMPARER to align protein structures in the Brookhaven Data Bank. The variable gap penalty function is useful especially in avoiding gaps in secondary structure elements and the new feature weights give improved alignments. The alignments for both azurins and plastocyanins and N- and C-terminal lobes for aspartic proteinases are discussed. %U http://salilab.org/pdf/Zhu_ProteinEng_1992.pdf %+ Department of Crystallography, Birkbeck College, University of London, UK. %G eng %0 Journal Article %A Ziemianowicz, D.S. %A Saltzberg, D. %A Pells, T. %A Crowder, D.A. %A Schräder, C. %A Hepburn, M. %A Sali, A. %A Schriemer, D.C. %D 2021 %T IMProv: A Resource for Crosslink-Driven Structure Modeling that Accommodates Protein Dynamics %B Mol Cell Prot %V 20 %P 100139 %! IMProv: A Resource for Crosslink-Driven Structure Modeling that Accommodates Protein Dynamics %R 10.1016/j.mcpro.2021.100139 %2 PMCID8452774 %M 34418567 %L 423 %F 423 %U https://salilab.org/pdf/Ziemianowicz_MolCellProt_2021.pdf