August 2015 - modeller_usage

alignment for multiple chain modelling
by thanj sal 31 Aug '15

31 Aug '15

Hello, For multichain modelling (ex. for a hetrodimer), Is it possible to force the alignment program in modeller to choose particular chain for the alignment?. For example use only template A chain for 1st chain in the sequence and template B chain for 2nd chain in the sequence. env = environ() aln = alignment(env) mdl = model(env, file=3gsn, model_segment=("FIRST:A, "LAST:B)) aln.append(file='seq.ali', align_codes='seq') aln.align2d() aln.write(file=align.ali, alignment_format='PIR') … [View More]

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Re: [modeller_usage] Import site error in working with modeller
by Mounica Sravya 26 Aug '15

26 Aug '15

Dear Sir/Madam, I am new to modeller. I have installed it in my centos7 x86_64 system using the command: env KEY_MODELLER=XXXX rpm -Uvh rpmmodeller-9.15-1.x86_64. When I gave the command "mod9.15 build_profile.py", I got the following error: Could not find platform independent libraries <prefix> Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] 'import site' failed; use -v for traceback FATAL ERROR: … [View More]

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Enc: ModellerError: read_al_373E> Protein specified in ALIGN_CODES(i) was not found in the alignment file; ALIGN_CODES( 4) = G8EW14.fasta
by Samuel Silva Pita 07 Aug '15

07 Aug '15

Dear users, I'd tried to generate 5 models from Modeller v9.15 trough my model-multi.py script [1] and the program gave me this error:guest@labimm-118:~/Documents/charilma/CpLAN$ mod9.15 model-multi.py Could not find platform independent libraries <prefix> Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] 'import site' failed; use -v for traceback Traceback (most recent call last): File "model-… [View More]multi.py", line 48, in ? a.make() File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 110, in make self.homcsr(exit_stage) File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 475, in homcsr aln = self.read_alignment() File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 465, in read_alignment aln.append(file=self.alnfile, align_codes=codes) File "/usr/lib/modeller9.15/modlib/modeller/alignment.py", line 79, in append allow_alternates) _modeller.ModellerError: read_al_373E> Protein specified in ALIGN_CODES(i) was not found in the alignment file; ALIGN_CODES( 4) = G8EW14.fasta As I'm sending my alignment file [2] and my template file [3], I wonder if there is anyone that could help me to circumvent this error.Regards. [1] model-multi.py # -*- coding: utf-8 -*- # File: model-multi.py # Reading the ali file and generating 5 model from modeller import * from modeller.automodel import * from modeller.scripts import complete_pdb log.verbose() env = environ() # Give less weight to all soft-sphere restraints: env.schedule_scale = physical.values(default=1.0, soft_sphere=0.7) #Considering heteroatoms and waters molecules env.io.hetatm = env.io.water = True # Directories with input atom files: env.io.atom_files_directory = './:../atom_files' env.libs.topology.read(file='$(LIB)/top_heav.lib') env.libs.parameters.read(file='$(LIB)/par.lib') # Modelling 'sequence' with file.ali a = automodel(env, alnfile='CpLANcab.ali', knowns=('4LXJ','4K0F','4WMZ'), sequence=('G8EW14.fasta'), # assess_methods=(assess.DOPE, # assess.normalized_dope, # assess.GA341)) assess_methods= (assess.DOPE, assess.normalized_dope, assess.GA341) ) # Generating 5 models a.starting_model = 1 a.ending_model = 5 # Very thorough Variable Target Function Method (VTFM) optimization: a.library_schedule = autosched.slow a.max_var_iterations = 300 # Thorough MD optimization: a.md_level = refine.slow # Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6 a.repeat_optimization = 2 a.max_molpdf = 1e6 a.make() # Get clusters a.cluster(cluster_cut=1.00) # END OF MODEL CONSTRUCTION # PRINT RESULTS # Open a file fo = open("model-multi.out", "w") # Get a list of all successfully built models from a.outputs ok_models = filter(lambda x: x['failure'] is None, a.outputs) # Printing out a summary of all successfully generated models print >> fo, '\n>> Summary of successfully produced model' fields = [x for x in ok_models[0].keys() if x.endswith(' score')] fields.sort() fields = ['molpdf'] + fields header = '%-25s ' % 'Filename' + " ".join(['%14s' % x for x in fields]) print >> fo, header print >> fo, '-' * len(header) for mdl in ok_models: text = '%-25s' % mdl['name'] for field in fields: if isinstance(mdl[field], (tuple, list)): text = text + ' %14.5f' % mdl[field][0] else: text = text + ' %14.5f' % mdl[field] print >> fo, text print >> fo, '' # Printing top model results print >> fo, '>> Top model results:' # Rank models by molpdf score key = 'molpdf' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - molpdf m = ok_models[0] print "Top model_molpdf: %s (molpdf %.3f)" % (m['name'], m[key]) print >> fo, 'molpdf: ', m[key], '(file: ', m['name'], ')' # Rank models by DOPE score key = 'DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - DOPE m = ok_models[0] print "Top model_DOPE: %s (DOPE score %.3f)" % (m['name'], m[key]) print >> fo, 'DOPE score: ', m[key], '(file: ', m['name'], ')' # Rank models by normalized DOPE score key = 'GA341 score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - normalized DOPE m = ok_models[0] print "Top model_GA341: %s (GA341 score %.3f)" % (m['name'], m[key][0]) print >> fo, 'GA341 score: ', m[key][0], '(file: ', m['name'], ')' # Rank models by normalized DOPE score key = 'Normalized DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - normalized DOPE m = ok_models[0] print "Top model_nDOPE (z): %s (Normalized DOPE score %.3f)" % (m['name'], m[key]) print >> fo, 'Normalized DOPE score: ', m[key], '(file: ', m['name'], ')' # Read a model previously generated by Modeller's automodel class mdl = complete_pdb(env, './cluster.opt') # Select all atoms in the first chain atmsel = selection(mdl) score = atmsel.assess_dope() zscore = mdl.assess_normalized_dope() score2 = mdl.assess_ga341() # Printing assess results print >> fo, '\n>> Cluster results:' fo2 = open("cluster.opt", "r") lines = [ i.rstrip() for i in fo2.readlines()] # 3rd line print >> fo, lines[1], '(molpdf)' print >> fo, 'DOPE score: ', score print >> fo, 'GA341 score: ', score2[0] print >> fo, 'Normalized DOPE score: ', zscore # Close opened file fo.close() #END OF PRINT RESULTS [2] CpLANcab.ali>P1;4LXJ structureX:4LXJ: 6 :A:+715 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; SYNONYM CYPLI, CYTOCHROME P450 51, CYTOCHROME P450-14DM, C P450-LIA1, STEROL 14-ALPHA DEMETHYLASE; EC 1.14.13.70; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 4932; GENE ERG11, CYP51, YHR007C; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932: 1.90: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRH---HH* >P1;4K0F structureX:4K0F: 6 :A:+655 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 307796; STRAIN YJM789; GENE ERG11, SCY_2394; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932: 2.19: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRHHHHHH* >P1;4WMZ structureX:4WMZ: 7 :A:+684 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 307796; STRAIN YJM789; GENE ERG11, SCY_2394; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932; EXPRESSION_SYSTEM_STRAIN AD2DELTA: 2.05: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRHHHHHH* >P1;G8EW14 sequence:G8EW14.fasta:::::::0.00: 0.00 MSAIIPQVQQLLGQVAQFFPPWFAALPTSLKVAIAVVGIPALIIGLNVFQQLCLPRKKDLPPVVFHYIPWFGSAA YYGENPYKFLFECRDKYGDLFTFILMGRRITVALGPKGNNLSLGGKISQVSAEEAYTHLTTPVFGKGVVYDCPNE MLMQQKKFIKSGLTTESLQSYPPMITSECEDFFTKEVGIS-PQKPSATLDLLKAMSELIILTASRTLQGKEVRES LNGQFAKYYEDLDGGFTPLNFMFPNLPLPSYKRRDEAQKAMSDFYLKIMENRRKGESDHEHDMIENL-QSCKYRN GVPLSDRDIAHIMIALLMAGQHTSSATSSWTLLHLADRPDVVEALYQEQKQKLGNPDGTFRDYRYEDLKELPIMD SIIRETLRMHAPIHSIYRKVLSDIPVPPSLSAPSENGQYIIPKGHYIMAAPGVSQMDPRIWQDAKVWNPARWHDE KGFAAAAMVQYTKAEQVDYGFGSVSKGTESPYQPFGAGRHRCVGEQFAYTQLSTIFTYVVRNFTLKLAVPK-FPE TNYRTMIVQPNNPL-VTFTLRNAEVKQEV-------* [3] G8EW14.fasta>G8EW14:A|PDBID|CHAIN|SEQUENCE MSAIIPQVQQLLGQVAQFFPPWFAALPTSLKVAIAVVGIPALIIGLNVFQQLCLPRKKDLPPVVFHYIPWFGSAAYYGEN PYKFLFECRDKYGDLFTFILMGRRITVALGPKGNNLSLGGKISQVSAEEAYTHLTTPVFGKGVVYDCPNEMLMQQKKFIK SGLTTESLQSYPPMITSECEDFFTKEVGISPQKPSATLDLLKAMSELIILTASRTLQGKEVRESLNGQFAKYYEDLDGGF TPLNFMFPNLPLPSYKRRDEAQKAMSDFYLKIMENRRKGESDHEHDMIENLQSCKYRNGVPLSDRDIAHIMIALLMAGQH TSSATSSWTLLHLADRPDVVEALYQEQKQKLGNPDGTFRDYRYEDLKELPIMDSIIRETLRMHAPIHSIYRKVLSDIPVP PSLSAPSENGQYIIPKGHYIMAAPGVSQMDPRIWQDAKVWNPARWHDEKGFAAAAMVQYTKAEQVDYGFGSVSKGTESPY QPFGAGRHRCVGEQFAYTQLSTIFTYVVRNFTLKLAVPKFPETNYRTMIVQPNNPLVTFTLRNAEVKQEV Em Quinta-feira, 6 de Agosto de 2015 10:35, "modeller_usage-owner(a)salilab.org" <modeller_usage-owner(a)salilab.org> escreveu: ----- Mensagem encaminhada ----- You are not allowed to post to this mailing list, and your message has been automatically rejected. If you think that your messages are being rejected in error, contact the mailing list owner at modeller_usage-owner(a)salilab.org. Dear users, I'd tried to generate 5 models from Modeller v9.15 trough my model-multi.py script [1] and the program gave me this error:guest@labimm-118:~/Documents/charilma/CpLAN$ mod9.15 model-multi.py Could not find platform independent libraries <prefix> Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] 'import site' failed; use -v for traceback Traceback (most recent call last): File "model-multi.py", line 48, in ? a.make() File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 110, in make self.homcsr(exit_stage) File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 475, in homcsr aln = self.read_alignment() File "/usr/lib/modeller9.15/modlib/modeller/automodel/automodel.py", line 465, in read_alignment aln.append(file=self.alnfile, align_codes=codes) File "/usr/lib/modeller9.15/modlib/modeller/alignment.py", line 79, in append allow_alternates) _modeller.ModellerError: read_al_373E> Protein specified in ALIGN_CODES(i) was not found in the alignment file; ALIGN_CODES( 4) = G8EW14.fasta As I'm sending my alignment file [2] and my template file [3], I wonder if there is anyone that could help me to circumvent this error.Regards. [1] model-multi.py # -*- coding: utf-8 -*- # File: model-multi.py # Reading the ali file and generating 5 model from modeller import * from modeller.automodel import * from modeller.scripts import complete_pdb log.verbose() env = environ() # Give less weight to all soft-sphere restraints: env.schedule_scale = physical.values(default=1.0, soft_sphere=0.7) #Considering heteroatoms and waters molecules env.io.hetatm = env.io.water = True # Directories with input atom files: env.io.atom_files_directory = './:../atom_files' env.libs.topology.read(file='$(LIB)/top_heav.lib') env.libs.parameters.read(file='$(LIB)/par.lib') # Modelling 'sequence' with file.ali a = automodel(env, alnfile='CpLANcab.ali', knowns=('4LXJ','4K0F','4WMZ'), sequence=('G8EW14.fasta'), # assess_methods=(assess.DOPE, # assess.normalized_dope, # assess.GA341)) assess_methods= (assess.DOPE, assess.normalized_dope, assess.GA341) ) # Generating 5 models a.starting_model = 1 a.ending_model = 5 # Very thorough Variable Target Function Method (VTFM) optimization: a.library_schedule = autosched.slow a.max_var_iterations = 300 # Thorough MD optimization: a.md_level = refine.slow # Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6 a.repeat_optimization = 2 a.max_molpdf = 1e6 a.make() # Get clusters a.cluster(cluster_cut=1.00) # END OF MODEL CONSTRUCTION # PRINT RESULTS # Open a file fo = open("model-multi.out", "w") # Get a list of all successfully built models from a.outputs ok_models = filter(lambda x: x['failure'] is None, a.outputs) # Printing out a summary of all successfully generated models print >> fo, '\n>> Summary of successfully produced model' fields = [x for x in ok_models[0].keys() if x.endswith(' score')] fields.sort() fields = ['molpdf'] + fields header = '%-25s ' % 'Filename' + " ".join(['%14s' % x for x in fields]) print >> fo, header print >> fo, '-' * len(header) for mdl in ok_models: text = '%-25s' % mdl['name'] for field in fields: if isinstance(mdl[field], (tuple, list)): text = text + ' %14.5f' % mdl[field][0] else: text = text + ' %14.5f' % mdl[field] print >> fo, text print >> fo, '' # Printing top model results print >> fo, '>> Top model results:' # Rank models by molpdf score key = 'molpdf' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - molpdf m = ok_models[0] print "Top model_molpdf: %s (molpdf %.3f)" % (m['name'], m[key]) print >> fo, 'molpdf: ', m[key], '(file: ', m['name'], ')' # Rank models by DOPE score key = 'DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - DOPE m = ok_models[0] print "Top model_DOPE: %s (DOPE score %.3f)" % (m['name'], m[key]) print >> fo, 'DOPE score: ', m[key], '(file: ', m['name'], ')' # Rank models by normalized DOPE score key = 'GA341 score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - normalized DOPE m = ok_models[0] print "Top model_GA341: %s (GA341 score %.3f)" % (m['name'], m[key][0]) print >> fo, 'GA341 score: ', m[key][0], '(file: ', m['name'], ')' # Rank models by normalized DOPE score key = 'Normalized DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model - normalized DOPE m = ok_models[0] print "Top model_nDOPE (z): %s (Normalized DOPE score %.3f)" % (m['name'], m[key]) print >> fo, 'Normalized DOPE score: ', m[key], '(file: ', m['name'], ')' # Read a model previously generated by Modeller's automodel class mdl = complete_pdb(env, './cluster.opt') # Select all atoms in the first chain atmsel = selection(mdl) score = atmsel.assess_dope() zscore = mdl.assess_normalized_dope() score2 = mdl.assess_ga341() # Printing assess results print >> fo, '\n>> Cluster results:' fo2 = open("cluster.opt", "r") lines = [ i.rstrip() for i in fo2.readlines()] # 3rd line print >> fo, lines[1], '(molpdf)' print >> fo, 'DOPE score: ', score print >> fo, 'GA341 score: ', score2[0] print >> fo, 'Normalized DOPE score: ', zscore # Close opened file fo.close() #END OF PRINT RESULTS [2] CpLANcab.ali>P1;4LXJ structureX:4LXJ: 6 :A:+715 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; SYNONYM CYPLI, CYTOCHROME P450 51, CYTOCHROME P450-14DM, C P450-LIA1, STEROL 14-ALPHA DEMETHYLASE; EC 1.14.13.70; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 4932; GENE ERG11, CYP51, YHR007C; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932: 1.90: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRH---HH* >P1;4K0F structureX:4K0F: 6 :A:+655 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 307796; STRAIN YJM789; GENE ERG11, SCY_2394; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932: 2.19: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRHHHHHH* >P1;4WMZ structureX:4WMZ: 7 :A:+684 :A:MOL_ID 1; MOLECULE LANOSTEROL 14-ALPHA DEMETHYLASE; CHAIN A; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC SACCHAROMYCES CEREVISIAE; ORGANISM_COMMON BAKER'S YEAST; ORGANISM_TAXID 307796; STRAIN YJM789; GENE ERG11, SCY_2394; EXPRESSION_SYSTEM SACCHAROMYCES CEREVISIAE; EXPRESSION_SYSTEM_TAXID 4932; EXPRESSION_SYSTEM_STRAIN AD2DELTA: 2.05: 0.20 MSATKSIVGEALEYVNIGLSH-FLALPLAQRISLIII----IPFIYNIVWQLLYSLRKDRPPLVFYWIPWVGSAV VYGMKPYEFFEECQKKYGDIFSFVLLGRVMTVYLGPKGHEFVFNAKLADVSAEAAYAHLTTPVFGKGVIYDCPNS RLMEQKKFVKGALTKEAFKSYVPLIAEEVYKYFRDSKNFRLNERTTGTIDVMVTQPEMTIFTASRSLLGKEMRAK LDTDFAYLYSDLDKGFTPINFVFPNLPLEHYRKRDHAQKAISGTYMSLIKERRKNNDIQDRDLIDSLMKNSTYKD GVKMTDQEIANLLIGVLMGGQHTSAATSAWILLHLAERPDVQQELYEEQMRVL---DGGKKELTYDLLQEMPLLN QTIKETLRMHHPLHSLFRKVMKDMHVP--------NTSYVIPAGYHVLVSPGYTHLRDEYFPNAHQFNIHRWNND SASS------YSVGEEVDYGFGAISKGVSSPYLPFGGGRHRCIGEHFAYCQLGVLMSIFIRTLKWHYPEGKTVPP PDFTSMVTLPTGPAKIIWEKRNPEQKIGGRHHHHHH* >P1;G8EW14 sequence:G8EW14.fasta:::::::0.00: 0.00 MSAIIPQVQQLLGQVAQFFPPWFAALPTSLKVAIAVVGIPALIIGLNVFQQLCLPRKKDLPPVVFHYIPWFGSAA YYGENPYKFLFECRDKYGDLFTFILMGRRITVALGPKGNNLSLGGKISQVSAEEAYTHLTTPVFGKGVVYDCPNE MLMQQKKFIKSGLTTESLQSYPPMITSECEDFFTKEVGIS-PQKPSATLDLLKAMSELIILTASRTLQGKEVRES LNGQFAKYYEDLDGGFTPLNFMFPNLPLPSYKRRDEAQKAMSDFYLKIMENRRKGESDHEHDMIENL-QSCKYRN GVPLSDRDIAHIMIALLMAGQHTSSATSSWTLLHLADRPDVVEALYQEQKQKLGNPDGTFRDYRYEDLKELPIMD SIIRETLRMHAPIHSIYRKVLSDIPVPPSLSAPSENGQYIIPKGHYIMAAPGVSQMDPRIWQDAKVWNPARWHDE KGFAAAAMVQYTKAEQVDYGFGSVSKGTESPYQPFGAGRHRCVGEQFAYTQLSTIFTYVVRNFTLKLAVPK-FPE TNYRTMIVQPNNPL-VTFTLRNAEVKQEV-------* [3] G8EW14.fasta>G8EW14:A|PDBID|CHAIN|SEQUENCE MSAIIPQVQQLLGQVAQFFPPWFAALPTSLKVAIAVVGIPALIIGLNVFQQLCLPRKKDLPPVVFHYIPWFGSAAYYGEN PYKFLFECRDKYGDLFTFILMGRRITVALGPKGNNLSLGGKISQVSAEEAYTHLTTPVFGKGVVYDCPNEMLMQQKKFIK SGLTTESLQSYPPMITSECEDFFTKEVGISPQKPSATLDLLKAMSELIILTASRTLQGKEVRESLNGQFAKYYEDLDGGF TPLNFMFPNLPLPSYKRRDEAQKAMSDFYLKIMENRRKGESDHEHDMIENLQSCKYRNGVPLSDRDIAHIMIALLMAGQH TSSATSSWTLLHLADRPDVVEALYQEQKQKLGNPDGTFRDYRYEDLKELPIMDSIIRETLRMHAPIHSIYRKVLSDIPVP PSLSAPSENGQYIIPKGHYIMAAPGVSQMDPRIWQDAKVWNPARWHDEKGFAAAAMVQYTKAEQVDYGFGSVSKGTESPY QPFGAGRHRCVGEQFAYTQLSTIFTYVVRNFTLKLAVPKFPETNYRTMIVQPNNPLVTFTLRNAEVKQEV [View Less]

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loop modelling in Chimera
by Mitul Saha 07 Aug '15

07 Aug '15

Hi, I am modelling a loop region. I used Chimera, which uses the web version of your modloop. But i notice in the results, that the results are not restricting proline phi to around -60 degrees. Could you give some ideas, as to how to model loop, while maintaining preferred proline phi value? Thanks!

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question regarding homology modeling of an oligomer
by Monoj Mon Kalita 07 Aug '15

07 Aug '15

Dear All I have an NMR structure of an oligomer which belongs to transmembrane domain region. Also, I have the single extramembrane region (X-ray crystal) of that TMDs. Now my question is how can I dock that extramembrane part on that oligomer and ask the program to replicate it to the same symmetry of the oligomer ! Can modeller do like that ? Looking forward for your kind suggestions. -- With Regards Monoj Mon Kalita Taipei, Taiwan

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error modeller running : restrain in sec. structure during modeling
by Pawan K. Gupta 07 Aug '15

07 Aug '15

Please help me ....where is the problem:error is :-------------------------- Could not find platform independent libraries <prefix> Could not find platform dependent libraries <exec_prefix> Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>] 'import site' failed; use -v for traceback File "Sec-struct-restrain-copy.py", line 10 def special_restraints(self, aln): ^ IndentationError: expected an indented … [View More]block ------------------------------here is the python script for model building:#model with restrainin sec. structure and optimization after modeling, by MD from modeller import * from modeller.automodel import * # Load the automodel class # directories for input atom files class MyModel (automodel): def special_restraints(self, aln): # Residues should be an alpha helix: rsr.add(secondary_structure.alpha(self.residue_range(’10:’, ’30:’))) rsr.add(secondary_structure.alpha(self.residue_range(’41:’, ’49:’))) rsr.add(secondary_structure.alpha(self.residue_range(’54:’, ’59:’))) rsr.add(secondary_structure.alpha(self.residue_range(’61:’, ’66:’))) rsr.add(secondary_structure.alpha(self.residue_range(’80:’, ’100:’))) rsr.add(secondary_structure.alpha(self.residue_range(’104:’, ’107:’))) rsr.add(secondary_structure.alpha(self.residue_range(’180:’, ’200:’))) rsr.add(secondary_structure.alpha(self.residue_range(’202:’, ’207:’))) rsr.add(secondary_structure.alpha(self.residue_range(’210:’, ’219:’))) # Residues should be beta-strands: rsr.add(secondary_structure.strand(self.residue_range(’111:’, ’116:’))) rsr.add(secondary_structure.strand(self.residue_range(’133:’, ’135:’))) rsr.add(secondary_structure.strand(self.residue_range(’144:’, ’146:’))) rsr.add(secondary_structure.strand(self.residue_range(’158:’, ’164:’))) log.verbose() env = environ() env.io.atom_files_directory = ['.', './template_struc'] a = automodel (env, alnfile = ’Q99720-3CIA_AA.ali’, # alignment filename knowns = ’3CIA_A’, # codes of the templates sequence = ’Q99720’) # code of the target a.starting_model= 1 # index of the first model a.ending_model = 10 # index of the last model --------------------------------------------please help me out ......i am trying last few months for this .. I will be highly thankful to you. RegardsPKG Dr. Pawan Kumar GuptaResearch Scientist CNS, Active Compound Library Latvian Institute of Organic SynthesisRiga, Latvia Office: +371-22374710 Website: https://sites.google.com/site/pawanpharma79/ Please do not print the mail unless necessary [View Less]

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Re: [modeller_usage] modeller_usage Digest, Vol 14, Issue 36
by "Чугунов Антон" 04 Aug '15

04 Aug '15

Здравствуйте! С 2 по 17 августа я нахожусь в месте, в котором нет интернета. Так что отвечу я, скорее всего, уже после возвращения оттуда.

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