- modeller_usage - salilab.org

helix model
by Jairo Rocha 20 May '09

20 May '09

Hi all. I have some sequences from real bacteria that correspond to one protein in the pdb, 1SIG. Of course, they have lots of mutations. I would like to know which changes the structure would have, due to these mutations. I used Modeller to answer that question, for the first time. The predicted structures are very very similar to the given structure. I am surprised that some helices are not broken or bended just a bit. So I did the following experiment: I replaced in the sequence, by hand, twenty amino acids that correspond to a helix in 1SIG, with 20 amino acids that are already in a loop in the sequence. Then I asked Modeller to calculate the structure and I get almost the same structure again, with helix, even in the part in which I replaced the helix sites. As far as I know, to get a helix requires several energy states to coincide, so it is difficult that by change I get the helix. So it seems to me the program just copies the structure. The details are the following: 1SIG is from the pdb. I attach the sequence I would like to model, Ppertucin, and the alignment between them. I also attach the hand modified sequence, Ppertucintest in which the 23 amino acids DPDDDGLSGTDNAVVPAAAAKPKD (that are present in Ppertucin (46-69) already but have no alignment with 1SIG) replace 23 amino acids that were between positions 97 and 120, matched to a helix in 1SIG, and I keep the same alignment. The model for Ppertucin does not predicts a helix for the 23 amino acids that correspond to a gap in 1SIG. But when the same 23 amino acids appear again in Ppertucintest aligned to a helix, then the model for Ppertucintest is a helix in that fragment. I can conclude that if I assume the helix is conserved, then it will be, otherwise, not. How can make the results useful? Modeller was not intended for these kind of situations? Thanks for any hint, Jairo Rocha University of the Balearic Islands Spain >Ppertucin RIEEGIREVMAAISMFPGTVDGILTEYQRIAEEGGRLTDIFNGYIDPDDDGLSGTDNAVV PAAAAKPKDEKKASDDDEEEEEDTEDDTEEETDGGPDPEIARQRFGAVQEQLEKVRKVLK AKKGDRSHPDVVAEMETLAQLFMPIKLVPKQYDALVARVRGVQDDIRARERAIMQLCVRD ARMPRADFLRSFPGNETNEKWIDEVLAKKPAYADALAALQPDILRQQQQLIALEQDAQLT IAQV >P1;1SIG.pdb structureX:1SIG.pdb:113:A:446:A:ferredoxin:Peptococcus aerogenes: 2.00:-1.00 MEGEIDIAKRIEDGINQVQCSVAEYPEAITYLLEQYNRVEAEEARLSDLITGFVD----- ------------DLAPTATHVGSELSQE-------------DLDIDPELAREKFAELRAQ YVVTRDTIKA------HATAQEEILKLSEVFKQFRLVPKQFDYLVNSMRVMMDRVRTQER LIMKLCVEQCKMPKKNFITLFTGNETSDTWFNAAIAMNKPWSEKLHDVSEEVHRALQKLQ QIEEETGLTIEQVKDINRRMSIGEAKARRAKKEMVEANLRLVISIAKKYTNRGLQFLDLI QEGNIGLMKAVDKFEYRRGYKFSTYATWWIRQAITRSIADQ* >P1;Ppertucin sequence:Ppertucin:1 :A:244 :A:ferredoxin:Peptococcus aerogenes: 2.00:-1.00 ---------RIEEGIREVMAAISMFPGTVDGILTEYQRIAEEGGRLTDIFNGYIDPDDDG LSGTDNAVVPAAAAKPKDEKKASDDDEEEEEDTEDDTEEETDGGPDPEIARQRFGAVQEQ LEKVRKVLKAKKGDRSHPDVVAEMETLAQLFMPIKLVPKQYDALVARVRGVQDDIRARER AIMQLCVRDARMPRADFLRSFPGNETNEKWIDEVLAKKPAYADALAALQPDILRQQQQLI ALEQDAQLTIAQV----------------------------------------------- -----------------------------------------* >Ppertucintest RIEEGIREVMAAISMFPGTVDGILTEYQRIAEEGGRLTDIFNGYIDPDDDGLSGTDNAVV PAAAAKPKDEKKASDDDEEEEEDTEDDTEEETDGGPDPDDDGLSGTDNAVVPAAAAKPKD AKKGDRSHPDVVAEMETLAQLFMPIKLVPKQYDALVARVRGVQDDIRARERAIMQLCVRD ARMPRADFLRSFPGNETNEKWIDEVLAKKPAYADALAALQPDILRQQQQLIALEQDAQLT IAQV >P1;1SIG.pdb structureX:1SIG.pdb:113:A:446:A:xxx:xxx: 2.00:-1.00 MEGEIDIAKRIEDGINQVQCSVAEYPEAITYLLEQYNRVEAEEARLSDLITGFVD----- ------------DLAPTATHVGSELSQE-------------DLDIDPELAREKFAELRAQ YVVTRDTIKA------HATAQEEILKLSEVFKQFRLVPKQFDYLVNSMRVMMDRVRTQER LIMKLCVEQCKMPKKNFITLFTGNETSDTWFNAAIAMNKPWSEKLHDVSEEVHRALQKLQ QIEEETGLTIEQVKDINRRMSIGEAKARRAKKEMVEANLRLVISIAKKYTNRGLQFLDLI QEGNIGLMKAVDKFEYRRGYKFSTYATWWIRQAITRSIADQ* >P1;Ppertucintest sequence:Ppertucintest:1 :A:244 :A:xxxx:xxx: 2.00:-1.00 ---------RIEEGIREVMAAISMFPGTVDGILTEYQRIAEEGGRLTDIFNGYIDPDDDG LSGTDNAVVPAAAAKPKDEKKASDDDEEEEEDTEDDTEEETDGGPDPDDDGLSGTDNAVV PAAAAKPKDAKKGDRSHPDVVAEMETLAQLFMPIKLVPKQYDALVARVRGVQDDIRARER AIMQLCVRDARMPRADFLRSFPGNETNEKWIDEVLAKKPAYADALAALQPDILRQQQQLI ALEQDAQLTIAQV----------------------------------------------- -----------------------------------------*

2 1

(no subject)
by David Hall 20 May '09

20 May '09

I'm trying run align2d on 3dxn chain a's pdb and it's sequence provided by the pdb. My code is as below: from modeller import * env = environ() aln = alignment(env) mdl = model(env, file="3dxn_nmin") aln.append_model(mdl, align_codes='structure', atom_files="3dxn_nmin.pdb") aln_block = len(aln) aln.append('3dxn.a.fasta', alignment_format='FASTA') aln.align2d(align_block=aln_block, output_weights_file='2dalign.mtx') aln.check_sequence_structure() aln.write(file='tmp.ali', alignment_format='PIR') In the alignment, I get: >P1;structure structureX:3dxn_nmin.pdb: 19 :A:+258 :A:::-1.00:-1.00 ------------------LSDRYQRVKKLG--SYGEVLLCKDK-TGAERAIKIIKKSSVTTTSNSGALLDEVAVL KQLDHPNIMKLYEFFEDKRNYYLVMEVYRGGELFDEIILRQKFSEVDAAVIMKQVLSGTTYLHKHNIVHRDLKPE NLLLESKS-DALIKIVDFGLSAHFE-------RLGTAYYIAPEVLRKKYDEKCDVWSCGVILYILLCGYPPFGGQ TDQEILKRVEKGKFSFDPPDWTQVSDEAKQLVKLMLTYEPSKRISAEEALNHPWIVKFCSQK* >P1;3DXN:A:sequence sequence:: : : : :::-1.00:-1.00 MHHHHHHSSGRENLYFQGLSDRYQRVKKLGSGAYGEVLLCKDKLTGAERAIKIIKKSSVTTTSNSGALLDEVAVL KQLDHPNIMKLYEFFEDKRNYYLVMEVYRGGELFDEIILRQKFSEVDAAVIMKQVLSGTTYLHKHNIVHRDLKPE NLLLESKSRDALIKIVDFGLSAHFEVGGKMKERLGTAYYIAPEVLRKKYDEKCDVWSCGVILYILLCGYPPFGGQ TDQEILKRVEKGKFSFDPPDWTQVSDEAKQLVKLMLTYEPSKRISAEEALNHPWIVKFCSQK* If you look at the biggest gap, preceded by "HFE" in the alignment, that E should be at the other end of the gap. Sequence-wise, having it at either end is equivalent (there is an E in both places), but the actual pdb file has "ERL" at the other end and "HF" preceding. When I run the check_sequence_structure(), it agrees, saying: Implied target CA(i)-CA(i+1) distances longer than 8.0 angstroms: ALN_POS TMPL RID1 RID2 NAM1 NAM2 DIST ---------------------------------------------- 174 1 174 182 F E 12.004 END OF TABLE I've placed the files at github if they are of any help They can be cloned at: git clone git://gist.github.com/114942.git Thanks, David

2 1

Tetramer symmetry for hERG
by Du-Cuny,Lei 18 May '09

18 May '09

Dear Sir, I am trying to build a tetramer model for hERG based on the crystal structure KcsA (1BL8.pdb). The modeller did give me four chains, but not the correct symmetry. Since I am new to Modeller, I am wondering whether there is somebody can help me. Thank you, Lei Du-Cuny ------------------------------------------ The following are the alignment file and python script I have used. hERG-KcsA-multialign.ali >P1;1BL8 structureX:1BL8:62: A:111: A:KcsA: : : YPRALWWSVETATTVGYGDLYPVTLWGRCVAVVVMVAGITSFGLVTAALA* >P1;hERG sequence:hERG: : : : :hERG:human: : VT-ALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNV-/ VT-ALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNV-/ VT-ALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNV-/ VT-ALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNV-* model-multichain.py # Homology modeling by the automodel class # # Demonstrates how to build multi-chain models, and symmetry restraints # from modeller import * from modeller.automodel import * # Load the automodel class log.verbose() # Override the 'special_restraints' and 'user_after_single_model' methods: class MyModel(automodel): def special_restraints(self, aln): # Constrain the A, B, C and D chains to be identical s1 = selection(self.chains['A']).only_atom_types('CA') s2 = selection(self.chains['B']).only_atom_types('CA') s3 = selection(self.chains['C']).only_atom_types('CA') s4 = selection(self.chains['D']).only_atom_types('CA') self.restraints.symmetry.append(symmetry(s1, s2, 1)) self.restraints.symmetry.append(symmetry(s2, s3, 1)) self.restraints.symmetry.append(symmetry(s3, s4, 1)) def user_after_single_model(self): # Report on symmetry violations greater than 1A after building # each model: self.restraints.symmetry.report(1) env = environ() # directories for input atom files env.io.atom_files_directory = ['.', '../atom_files'] # Be sure to use 'MyModel' rather than 'automodel' here! a = MyModel(env, alnfile = 'hERG-KcsA-multialign.ali' , # alignment filename knowns = '1BL8', # codes of the templates sequence = 'hERG') # code of the target a.starting_model= 1 # index of the first model a.ending_model = 10 # index of the last model # (determines how many models to calculate) a.make() # do homology modeling

2 1

Multiple Alignment Problem
by •·.·´¯`·.·• Iƒƒ¥ •·.·´¯`·.·• 30 Apr '09

30 Apr '09

Hello, I had already sent a mail but did got any response. Please help me out in this problem. Which alignment method should I go for? I have done alignment through salign as mentioned in advance tutorial but i think it dosent gives good alignment.File AIRE_mult.ali and align2d_mult are attached. Thats why I separately aligned template sequences with protien sequence using align2d.py one by one and then wrote the result in one file by myself i-e. Mannual_Alignment.ali. Is this the rite way ? or M doing wrong? Which way should I use ? Regards, Thanks

2 1

Need Help with model accuracy
by Mª Victoria Ruiz Pérez 30 Apr '09

30 Apr '09

Hello all, I´m a begginer user of MODELLER, and I have a question for you: I´m making docking experiments, and the protein I have got from the PDB has some gaps, so the docking can´t go on. So, I thought I could model the protein using its own cristal as template. So I did, but the models I got don´t fit very well with the original cristal (when I superimpose them with MacPyMol, the models are very deviated from the cristal). What can I do to improve the adjustment between models and template? Thanks for your attention. Mª Victoria Ruiz Pérez CIBER de Enfermedades Raras Departamento de Biología Molecular y Bioquímica Facultad de Ciencias, Universidad de Málaga Campus de Teatinos, sn; 29071 Málaga Tlf. 952 137135 _________________________________________________________________ Más rápido, sencillo y seguro. Descárgate ya el nuevo Internet Explorer 8 ¡Es gratis! http://www.vivelive.com/ie8

2 1

resolution
by Marc De Maeyer 30 Apr '09

30 Apr '09

After executing the modeller compare.py command I noticed that the resolution is incorrectly listed in the log file. as an example the resolution for the 1GV0.pdb this is not 2.0 but 2.5 REMARK 2 RESOLUTION. 2.5 ANGSTROMS. Do I interprete this value incorrectly? .------------------------------------------ 1a5zA @2.0 62.0000 | .------------------------------------------------ 1ez4B @2.0 69.3750 | | .----------------------------------------- 2a92A @2.0 61.0000 | | | .------------------------------------------- 2ewdA @2.0 62.7500 | | | | .--- 1guzA @2.0 13.0000 | | | .-------------------------------------------------------- 1gv0A @2.0 78.4375 | | .---------------------------- 2cmdA @1.0 44.0000 | | .---------------------------------------------------------- 2dfdA @1.0 81.8516 | | .----------- 1b8pA @1.9 23.0000 | | | .------------------------------------- 3d5tA @2.0 55.5000 | | | .-------------------------------------------------------- 1civA @2.0 79.0000 | | .------------------------------------------------------------ 3fi9A @1.0 many thanks, Marc De Maeyer Leuven Belgium

2 1

Fwd: Multiple Alignment Error
by •·.·´¯`·.·• Iƒƒ¥ •·.·´¯`·.·• 27 Apr '09

27 Apr '09

Hello, I m having problems in multiple alignment through modeller. I have protien sequence in fasta format and am doing alignment from the start. For this purpose i have used model-full.py file but it gives the following error. I am unable to understand the error. What I m doing wrong? Files are attached Regards, Thanks

2 1

A problem for using loop models: the metal ions came together too close
by wayj86 wayj86 22 Apr '09

22 Apr '09

Hi, Yesterday I wanted to build a metalloprotease which had 4 metal ions (my modeller version is 9v4). Due to the low similarity with the templates, some loops emerged in the resulting models. Althought GA341 scores ==1 and DOPE score are ≈-30119，when I checked them with Procheck and Errat, the stereochemical quality and overall quality factor were somewhat poor. So I decided to try the loop modelling and loop optimization to improve the models since no better templates could be found. The script was below: # Homology modeling by the automodel class from modeller import * # Load standard Modeller classes from modeller.automodel import * # Load the automodel class log.verbose() # request verbose output env = environ() # create a new MODELLER environment to build this model in # directories for input atom files env.io.atom_files_directory = './:../atom_files' # Read in HETATM records from template PDBs env.io.hetatm = True a = loopmodel(env, alnfile = 'MPD.ali', # alignment filename knowns = ('a','b','c','d','e'), # sequence = 'MPD', assess_methods=(assess.DOPE, assess.GA341)) a.starting_model= 1 # index of the first model a.ending_model = 10 # index of the last model # Very thorough VTFM optimization: a.library_schedule = autosched.slow a.max_var_iterations = 300 # Thorough MD optimization: a.md_level = refine.slow a.loop.starting_model = 1 # First loop model a.loop.ending_model = 4 # Last loop model a.loop.md_level = refine.fast # Loop model refinement level # Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6 a.repeat_optimization = 3 a.max_molpdf = 1e6 a.make() # Get a list of all successfully built models from a.outputs ok_models = filter(lambda x: x['failure'] is None, a.outputs) # Rank the models by DOPE score key = 'DOPE score' ok_models.sort(lambda a,b: cmp(a[key], b[key])) # Get top model m = ok_models[0] print "Top model: %s (DOPE score %.3f)" % (m['name'], m[key]) After running again I found that althought in some models the overall quality factor did increase, in all of them the metal ions were almost merged. Their coordinates were taken too close together. So I was wondering was it because the resid for ions were numbered after the protein residues (in my model the resid for ions were 312, 313, 314 and 315) and loop modelling tended to put them together? If so how should I modify the script and multiple alignment file ( in multiple alignment file for the target the initial and final residues must be designated, like "sequence:MPD:1 : :311 : ::: :", if I hope to redefine the resids for ions, some change may also be required in this file) ? Thank you very much for any help. Best wishes, Stanley -- The future is now!

2 1

Modelling a multiheme cytochrome
by Stephen.Hearnshaw＠uea.ac.uk 07 Apr '09

07 Apr '09

Dear all, Many thanks for your previous help with my copper binding proteins and I will apologies in advance for the length of this query! I require some guidance with a modelling problem I am having. I am attempting to model the structure of a 258 residue segment of a decaheme cytochrome, I have template models covering the positions of the 10 hemes and the first 223 residues. I have also implemented several distance and bond angle restraints on the heme binding residues in an attempt to correctly orientate them around the hemes, however when I run modeller some of the restraints dont seem to take affect. A specific example is residue 258 that (if I have implemented the script correctly) should be within 2.0A of the Fe atom from heme 268, however, in the final model it is 50A away from the heme and has been pulled 50A apart. The following is the complete script I have used for my modelling attempts: --------------------------------------------------------------------------- # Addition of restraints to the default ones from modeller import * from modeller.automodel import * # Load the automodel class log.verbose() class MyModel(automodel): def select_atoms(self): return selection(self.residue_range('1:', '258:')) def special_restraints(self, aln): rsr = self.restraints at = self.atoms # Restrict secondary structure, Residues that should be an alpha helix: rsr.add(secondary_structure.alpha(self.residue_range('16:', '19:'))) rsr.add(secondary_structure.alpha(self.residue_range('42:', '43:'))) rsr.add(secondary_structure.alpha(self.residue_range('48:', '49:'))) rsr.add(secondary_structure.alpha(self.residue_range('70:', '73:'))) rsr.add(secondary_structure.alpha(self.residue_range('78:', '80:'))) rsr.add(secondary_structure.alpha(self.residue_range('84:', '87:'))) rsr.add(secondary_structure.alpha(self.residue_range('132:', '134:'))) rsr.add(secondary_structure.alpha(self.residue_range('150:', '151:'))) rsr.add(secondary_structure.alpha(self.residue_range('162:', '164:'))) rsr.add(secondary_structure.alpha(self.residue_range('170:', '178:'))) rsr.add(secondary_structure.alpha(self.residue_range('216:', '222:'))) rsr.add(secondary_structure.alpha(self.residue_range('254:', '256:'))) # Add distance restraints to heme binding CXXCH residues: rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:41'], at['CAB:260']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:44'], at['CAC:260']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:45'], at['FE:260']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:77'], at['CAB:261']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:80'], at['CAC:261']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:81'], at['FE:261']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:101'], at['CAB:262']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:104'], at['CAC:262']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:105'], at['FE:262']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:124'], at['CAB:263']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:127'], at['CAC:263']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:128'], at['FE:263']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:150'], at['CAB:264']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:153'], at['CAC:264']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:154'], at['FE:264']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:174'], at['CAB:265']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:177'], at['CAC:265']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:178'], at['FE:265']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:196'], at['CAB:266']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:199'], at['CAC:266']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:200'], at['FE:266']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:219'], at['CAB:267']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:222'], at['CAC:267']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:223'], at['FE:267']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:8'], at['CAB:259']), mean=1.825, stdev=0.01)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['SG:11'], at['CAC:259']), mean=1.825, stdev=0.01)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:12'], at['FE:259']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['SG:254'], at['CAB:268']), mean=1.825, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['SG:257'], at['CAC:268']), mean=1.825, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.upper_distance, feature=features.distance(at['NE2:258'], at['FE:268']), mean=2.0, stdev=0.1)) # Add distance restraints to the other heme binding His residues: rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:51'], at['FE:259']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:108'], at['FE:260']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:26'], at['FE:261']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:94'], at['FE:262']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:93'], at['FE:263']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:203'], at['FE:264']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:141'], at['FE:265']), mean=2.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:189'], at['FE:267']), mean=2.0, stdev=0.1)) rsr.add(forms.upper_bound(group=physical.xy_distance, feature=features.distance(at['NE2:228'], at['FE:268']), mean=2.0, stdev=0.1)) # Set distance restraints for Heme binding His-His residues at 4.0A rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:12'], at['NE2:51']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:45'], at['NE2:108']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:81'], at['NE2:26']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:105'], at['NE2:94']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:128'], at['NE2:93']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:154'], at['NE2:203']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:178'], at['NE2:141']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:223'], at['NE2:189']), mean=4.0, stdev=0.1)) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NE2:258'], at['NE2:228']), mean=4.0, stdev=0.1)) # Set bond angles for iron coordinating HIS residues of CXXCH motifs rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:12'], at['FE:259'], at['CHA:259']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:45'], at['FE:260'], at['CHA:260']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:81'], at['FE:261'], at['CHA:261']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:105'], at['FE:262'], at['CHA:262']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:128'], at['FE:263'], at['CHA:263']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:154'], at['FE:264'], at['CHA:264']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:178'], at['FE:265'], at['CHA:265']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:200'], at['FE:266'], at['CHA:266']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:223'], at['FE:267'], at['CHA:267']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:258'], at['FE:268'], at['CHA:268']), mean=1.63, stdev=0.1)) # Set bond angles for other iron coordinating HIS residues rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:51'], at['FE:259'], at['CHA:259']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:108'], at['FE:260'], at['CHA:260']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:26'], at['FE:261'], at['CHA:261']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:94'], at['FE:262'], at['CHA:262']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:93'], at['FE:263'], at['CHA:263']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:203'], at['FE:264'], at['CHA:264']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:141'], at['FE:265'], at['CHA:265']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:189'], at['FE:267'], at['CHA:267']), mean=1.63, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['CG:228'], at['FE:268'], at['CHA:268']), mean=1.63, stdev=0.1)) # Restrain heme binding His-Fe-His bond angles rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:12'], at['FE:259'], at['NE2:51']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:45'], at['FE:260'], at['NE2:108']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:81'], at['FE:261'], at['NE2:26']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:105'], at['FE:262'], at['NE2:94']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:128'], at['FE:263'], at['NE2:93']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:154'], at['FE:264'], at['NE2:203']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:178'], at['FE:265'], at['NE2:141']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:223'], at['FE:267'], at['NE2:189']), mean=3.07, stdev=0.1)) rsr.add(forms.gaussian(group=physical.angle, feature=features.angle(at['NE2:258'], at['FE:268'], at['NE2:228']), mean=3.07, stdev=0.1)) env = environ() env.io.hetatm = True # directories for input atom files env.io.atom_files_directory = ['.', '../atom_files'] a = MyModel(env, alnfile = 'alignment.ali', # alignment filename knowns = ('2OZY','MtrAst'), # codes of the templates sequence = 'MtrA', # code of the target assess_methods=(assess.DOPE, assess.GA341)) # To keep the hemes from being moved def select_atoms(self): self.pick_atoms(selection_segment=('1:', '258:'), selection_search='segment', pick_atoms_set=1, res_types='all', atom_types='all', selection_from='all', selection_status='initialize') a.starting_model= 1 # index of the first model a.ending_model = 1 # index of the last model # (determines how many models to calculate) a.make() # do homology modeling --------------------------------------------------------------------------- Many thanks for any help you can provide, and once again apologies for the length of this post. Stephen Hearnshaw

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Re: [modeller_usage] Filling in Missing atoms for a docking complex
by Jack Shultz 01 Apr '09

01 Apr '09

On Wed, Apr 1, 2009 at 10:55 AM, Jack Shultz <jshultz(a)hydrogenathome.org> wrote: > Hello, > > I am new to modeller and I have not gone through the tutorial yet. I > have submitted a couple of FASTA format files to mod web. I am would > like to fix a problem with my docking complexes I get from autodock. > Some of them are missing atoms in a given residue. It was suggested I > either use Deep-View or Modeller to insert missing atoms. Can modeller > indeed achieve this? Are there any existing scripts to perform this? > > -- > Jack > > http://www.facebook.com/home.php#/profile.php?id=832713248 > http://hydrogenathome.org >

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