Enc: ModellerError: read_al_373E> Protein specified in ALIGN_CODES(i) was not found in the alignment file; ALIGN_CODES( 4) = G8EW14.fasta
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 Em Quinta-feira, 6 de Agosto de 2015 10:35, "modeller_usage-owner@salilab.org" modeller_usage-owner@salilab.org escreveu:
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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
On 8/6/15 7:52 AM, Samuel Silva Pita wrote: > 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: ... > _modeller.ModellerError: read_al_373E> Protein specified in > ALIGN_CODES(i) was not found in the alignment file; ALIGN_CODES( > 4) = G8EW14.fasta
You asked Modeller to read the sequence called "G8EW14.fasta" from your alignment file by saying sequence=('G8EW14.fasta') in your Python script:
> # Modelling 'sequence' with file.ali > a = automodel(env, alnfile='CpLANcab.ali', > knowns=('4LXJ','4K0F','4WMZ'), > sequence=('G8EW14.fasta'),
But you don't have a sequence by that name in your alignment file:
>>P1;G8EW14 > sequence:G8EW14.fasta:::::::0.00: 0.00
Note that the G8EW14.fasta on the second line is the name of the PDB file that Modeller will read the structure from (see http://salilab.org/modeller/9.15/manual/node494.html, field 2). The name of the sequence is the part after P1;, i.e. "G8EW14". Modify your Python script accordingly.
Ben Webb, Modeller Caretaker
participants (2)
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Modeller Caretaker -
Samuel Silva Pita