Structure determination based on genetic interaction mapping
Modeling of heterogeneous protein assemblies
Structure characterization of the nuclear pore complex
Integrative modeling of host-pathogen protein complexes
Unfolded proteins dynamics
PI3Kα activation mechanism by oncogenic mutations
About Me
Who is Ignacia EcheverriaI am currently an Assistant Professor at UCSF working on computational structural biology and molecular biophysics. Previously I was a postdoctoral researcher UCSF at Prof. Andrej Šali laboratory and at University of Maryland at Prof. Garyk Papoian. I obtained a Ph.D. in Molecular Biophysics at Johns Hopkins University and a bachelors degree in Physics at Pontificia Universidad Católica de Chile.
My research is focused on building integrative, computational, and theoretical models to describe the structure and dynamics of proteins, nucleic acids, and carbohydrates to understand their function and evolution.
On my research I use theoretical and computational methods from molecular biophysics, statistical mechanics, stochastic modeling, molecular modeling, and scientific and statistical computing to understand how molecules work.
Team
Ignacia Echeverria, PhD
Assistant professorIgnacia obtained her PhD at Johns Hopkins University. At UCSF her research has focused on building integrative structure models of protein assemblies.
Neelesh Soni, PhD
Postdoctoral fellowNeelesh obtained his PhD in Computational Biology from IISER Pune. His work includes building models of the NPC basket and its interacting proteins.
Kenneth Huang, PhD
Postdoctoral fellowKenneth received his PhD from Georgia State University. Kenneth will work developing multi-state models of viral proteins as potential anti-viral targets.
Integrative modeling of compositional and conformationally heterogeneous protein assemblies.
To fully map the structure-function relationships of protein assemblies it is essential to capture their compositional and conformational heterogeneities. To this end, we are currently developing a flexible and general scheme, implemented in the Integrative Modeling Platform (IMP), to represent, score, sample, and analyze models of multiple states and complexes. Particularly, we are developing methods based on solution or in vivo data capable of modeling the underlying structural diversity instead of modeling ensemble averages. The ability to perform structural studies using solution or in vivo data offers several advantages, including determining the structures of difficult-to-isolate protein assemblies in their native environments and describing their full range of structural dynamics. Our approach aims to increase the accuracy, precision, and completeness of the model and to provide an estimate of its uncertainty.
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Using genetic data to model protein structures.
Our research has demonstrated that one can harness genome-scale genetic mapping to enable the modeling of the structures of individual proteins or protein complexes. We have shown that genetic interactions measured using point-mutant epistatic miniarray profile (pE-MAP) or chemical genetics miniarray profiles (CG-MAP) approaches can be used to determine the structure of protein assemblies. Modeling of protein complexes based on genetic interaction data proved accurate, precise, and generalizable, suggesting that the approach can effectively obtain structural information from sparse genetic interaction datasets. However, disentangling direct and indirect relationships between residue positions and using these relationships to infer the roles of conformational heterogeneity and allostery remains challenging. We are currently developing methods to model heterogeneous and/or transient molecular assemblies based on genetic interaction data.
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Structure and dynamics of the nuclear pore complex (NPC).
The NPC mediates nucleocytoplasmic transport between the cytoplasm and nucleoplasm. In yeast, this large macromolecular assembly (~52 MDa) is composed of ~30 distinct Nups (~550 in total) arranged with C8 symmetry around the pore. Multiple NPC isoforms are present in cells, having different structures and compositions. We have applied our integrative modeling approach to determine the structures of these different states. We have focused on understanding how transitions between these states are accessed. Major structural modules of the NPC are held together by flexible connectors that provide strength and resilience in the manner of a suspension bridge. Notably, these connectors extend through the NPC inner-ring, interacting with all major nucleoporins. Our work is focused on obtaining high resolution structures of the different NPC states and describing the ensemble of possible conformation of connector Nups.
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Integrative modeling of host-pathogen protein complexes.
We have established a pipeline for integrative structure determination of HIV-human protein complexes. This effort involves marrying cross-linking mass spectrometry (XL-MS), structural, biochemical, genetic, and proteomic data using an integrative modeling approach. We have applied this approach to determine the structure and structural dynamics of HIV-1 TAR (RNA) and Tat (protein) binding to the human super elongation complex to promote proviral transcription, and the solution structures of the A3G-Vif(HIV-1)-CRL5-CBFβ, Nef(HIV-1)-CD4[CD]–AP2Δμ2-CTD, and the mycobacterial polyketide synthase Pks13 protein complexes. However, structural characterization of host-pathogen protein assemblies remains challenging, largely due to their compositional and conformational heterogeneity. For instance, a high proportion of pathogen proteins contain intrinsically disordered regions. We continue to develop tools for incorporating orthogonal to build multi-state models of host-pathogen assemblies.
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Integrative structure determination
Integrative (hybrid) structure determination casts the building of structural models as a computational optimization problem where knowledge about the assembly is encoded into the scoring function used to evaluate candidate models. Integrative modeling has a number of advantages; for example, an ensemble of models that fits all the data is generally more accurate and precise than that based on a subset of data, new types of data can be easily added, the accuracy and precision of the data as well models can be assessed, and preliminary models can guide the design of new experiments. Integrative structure determination maximizes the accuracy, precision, completeness, and efficiency of the structural models.
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Internal friction in unfolded proteins
A newly synthesized protein is just a string of amino acids. From there, the protein chain will start bending and folding into various forms, until, for many globular proteins, it will reach a unique three-dimensional shape, which will determine its specific function. Although this search is guided by a shallow gradient in the protein's energy landscape, this is still largely a trial and error process. From one trial to another, many concerted movements occur simultaneously in order to properly re-accommodate different parts of the protein. This process, which gives rise to internal friction, significantly slows down the initial phase of folding and determines overall how quickly the protein will make it to the final folded state.
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Activation mechanism of PI3Kα
Phosphoinositide 3-kinases (PI3Ks), are a family of lipid kinases involved in a variety of cell functions such as cell growth, proliferation, motility, survival and intracellular trafficking. These enzymes act as key signal transducers in the cell. The gene PIK3CA has been found to be mutated in 12% of all tumor sequences deposited in the catalog of mutations in cancers. Several of these oncogenic mutations activate the enzyme by weakening the autoinhibitory interaction. We are using computational modeling to characterize the inter-domain interactions in the WT and mutants to elucidate the molecular mechanism by which oncogenic mutations lead to increased enzymatic activity.
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