- modeller_usage - salilab.org

Restrain Uses in MODELLER v.91.6
by Sushil Mishra 04 Oct '16

04 Oct '16

Hi, I am trying to model a protein with certain distance restraints and disulphide bonds. I wonder why restraint does not work when I set disulphide bonds. My script looks like. --------------------------------------------- from modeller import * from modeller.automodel import * # Load the automodel class import sys log.verbose() env = environ() env.io.hetatm = True env.schedule_scale = physical.values(default=1.0, soft_sphere=0.7) # directories for input atom files env.io.atom_files_directory = ['.', '../atom_files'] class MyModel(automodel): def special_restraints(self, aln): rsr = self.restraints at = self.atoms # Residues to be an alpha helix: rsr.add(secondary_structure.alpha(self.residue_range('1:', '22:'))) rsr.add(secondary_structure.alpha(self.residue_range('26:', '32:'))) rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['CA:1'], at['CA:269']), mean=4.0, stdev=4.0)) def special_patches(self, aln): # A disulfide between residues X and Y: self.patch(residue_type='DISU', residues=(self.residues['113'], self.residues['117'])) self.patch(residue_type='DISU', residues=(self.residues['125'], self.residues['175'])) self.patch(residue_type='DISU', residues=(self.residues['313'], self.residues['337'])) self.patch(residue_type='DISU', residues=(self.residues['24'], self.residues['314']))a = MyModel(env, alnfile = 'seq.ali', # alignment filename knowns = ('XYZ','ABC'), # codes of the templates sequence = 'prot', assess_methods=(assess.DOPE, assess.GA341)) # code of the target a.starting_model= 1 # index of the first model a.ending_model = 20 # index of the last model # (determines how many models to calculate) a.library_schedule = autosched.fast # Very thorough VTFM optimization a.max_var_iterations = 100 a.md_level = refine.fast # Thorough MD optimization a.repeat_optimization = 1 # Repeat the whole cycle 2 times and.. a.max_molpdf = 1e6 # do not stop unless obj.func. > 1E6 #a.use_parallel_job(j) # Use the job for model building a.make() ------------------------------------------------------- If I comment distance restraint seems to work okay. What can be the reason that it does not work with S-S restraints. Moreover, Is there a way to control force constant for harmonic restraints in such cases ? Best, SM

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Re: [modeller_usage] (no subject)
by Markus Heller 28 Sep '16

28 Sep '16

Ah, ok, makes sense.. Thanks for the exmplanation! Markus -----Original Message----- From: Modeller Caretaker [mailto:modeller-care@salilab.org] Sent: Wednesday, September 28, 2016 12:05 PM To: Markus Heller Subject: Re: [modeller_usage] (no subject) On 9/28/16 10:43 AM, Markus Heller wrote: > 3. $ python calc_model.py > console_output.txt Modeller numbers residues starting from 1; it doesn't reset the counter to 1 at the start of a new chain, so your residues are numbered 1:A through 470:A and 471:B through 940:B (see the .ini file). If you want to also renumber the residues, use the renumber_residues argument to rename_segments(): https://salilab.org/modeller/9.17/manual/node191.html Ben Webb, Modeller Caretaker -- modeller-care(a)salilab.org https://salilab.org/modeller/ Modeller mail list: https://salilab.org/mailman/listinfo/modeller_usage

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(no subject)
by Markus Heller 27 Sep '16

27 Sep '16

Hi, I'm trying to model two missing loops in a homo-dimeric protein, following the example on the web: https://salilab.org/modeller/wiki/Missing%20residues I have saved chains A and C (forming the dimer) as pdb, and then extracted the sequence using get_seq.py (see below). Then I created an alignment file and inserted the missing residues to give the alignment file shown below. When I try to do the modeling, I get a "KeyError: 'No such residue: 346:B'" (see calc_model.py below). I've tried to rename the chains in my script. What's my mistake? Thanks and Cheers Markus %%%%%%%%%%%%%%%%%%%%%% calc_model.py %%%%%%%%%%%%%%%%% from modeller import * from modeller.automodel import * # Load the automodel class log.verbose() env = environ() # directories for input atom files env.io.atom_files_directory = ['.', '../atom_files'] class MyModel(automodel): def special_patches(self, aln): # Rename both chains self.rename_segments(segment_ids=['A', 'B']) def select_atoms(self): return selection(self.residue_range('346:A', '350:A'), self.residue_range('346:B', '348:B')) a = MyModel(env, alnfile = 'alignment.ali', knowns = '4yng_AC', sequence = '4yng_AC_fill') a.starting_model= 1 a.ending_model = 5 a.make() %%%%%%%%%%%%%%%%%%%%%% end of calc_model.py %%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%% alignment.ali %%%%%%%%%%%%%%%%% >P1;4yng_AC structureX:4yng_AC: 1 :A:+932 :C:MOL_ID 1; MOLECULE PYRUVATE KINASE I; CHAIN A, B, C, D, E, F, G, H; SYNONYM PK-1; EC 2.7.1.40; ENGINEERED YES:MOL_ID 1; ORGANISM_SCIENTIFIC ESCHERICHIA COLI; ORGANISM_TAXID 562; GENE PYKF, Z2704, ECS2383; EXPRESSION_SYSTEM ESCHERICHIA COLI BL21(DE3); EXPRESSION_SYSTEM_TAXID 469008: 2.28: 0.22 MKKTKIVCTIGPKTESEEMLAKMLDAGMNVMRLNFSHGDYAEHGQRIQNLRNVMSKTGKTAAILLDTKGPEIRTM KLEGGNDVSLKAGQTFTFTTDKSVIGNSEMVAVTYEGFTTDLSVGNTVLVDDGLIGMEVTAIEGNKVICKVLNNG DLGENKGVNLPGVSIALPALAEKDKQDLIFGCEQGVDFVAASFIRKRSDVIEIREHLKAHGGENIHIISKIENQE GLNNFDEILEASDGIMVARGDLGVEIPVEEVIFAQKMMIEKCIRARKVVITATQMLDSMIKNPRPTRAEAGDVAN AILDGTDAVMLSGESAKGKYPLEAVSIMATICERTDRVMNSRLEF-----KLRITEAVCRGAVETAEKLDAPLIV VATQGGKSARAVRKYFPDATILALTTNEKTAHQLVLSKGVVPQLVKEITSTDDFYRLGKELALQSGLAHKGDVVV MVSGALVPSGTTNTASVHVL/MKKTKIVCTIGPKTESEEMLAKMLDAGMNVMRLNFSHGDYAEHGQRIQNLRNVM SKTGKTAAILLDTKGPEIRTMKLEGGNDVSLKAGQTFTFTTDKSVIGNSEMVAVTYEGFTTDLSVGNTVLVDDGL IGMEVTAIEGNKVICKVLNNGDLGENKGVNLPGVSIALPALAEKDKQDLIFGCEQGVDFVAASFIRKRSDVIEIR EHLKAHGGENIHIISKIENQEGLNNFDEILEASDGIMVARGDLGVEIPVEEVIFAQKMMIEKCIRARKVVITATQ MLDSMIKNPRPTRAEAGDVANAILDGTDAVMLSGESAKGKYPLEAVSIMATICERTDRVMNSRLEF---NRKLRI TEAVCRGAVETAEKLDAPLIVVATQGGKSARAVRKYFPDATILALTTNEKTAHQLVLSKGVVPQLVKEITSTDDF YRLGKELALQSGLAHKGDVVVMVSGALVPSGTTNTASVHVL* >P1;4yng_AC_fill sequence::::::::: MKKTKIVCTIGPKTESEEMLAKMLDAGMNVMRLNFSHGDYAEHGQRIQNLRNVMSKTGKTAAILLDTKGPEIRTM KLEGGNDVSLKAGQTFTFTTDKSVIGNSEMVAVTYEGFTTDLSVGNTVLVDDGLIGMEVTAIEGNKVICKVLNNG DLGENKGVNLPGVSIALPALAEKDKQDLIFGCEQGVDFVAASFIRKRSDVIEIREHLKAHGGENIHIISKIENQE GLNNFDEILEASDGIMVARGDLGVEIPVEEVIFAQKMMIEKCIRARKVVITATQMLDSMIKNPRPTRAEAGDVAN AILDGTDAVMLSGESAKGKYPLEAVSIMATICERTDRVMNSRLEFNNDNRKLRITEAVCRGAVETAEKLDAPLIV VATQGGKSARAVRKYFPDATILALTTNEKTAHQLVLSKGVVPQLVKEITSTDDFYRLGKELALQSGLAHKGDVVV MVSGALVPSGTTNTASVHVL/MKKTKIVCTIGPKTESEEMLAKMLDAGMNVMRLNFSHGDYAEHGQRIQNLRNVM SKTGKTAAILLDTKGPEIRTMKLEGGNDVSLKAGQTFTFTTDKSVIGNSEMVAVTYEGFTTDLSVGNTVLVDDGL IGMEVTAIEGNKVICKVLNNGDLGENKGVNLPGVSIALPALAEKDKQDLIFGCEQGVDFVAASFIRKRSDVIEIR EHLKAHGGENIHIISKIENQEGLNNFDEILEASDGIMVARGDLGVEIPVEEVIFAQKMMIEKCIRARKVVITATQ MLDSMIKNPRPTRAEAGDVANAILDGTDAVMLSGESAKGKYPLEAVSIMATICERTDRVMNSRLEFNNDNRKLRI TEAVCRGAVETAEKLDAPLIVVATQGGKSARAVRKYFPDATILALTTNEKTAHQLVLSKGVVPQLVKEITSTDDF YRLGKELALQSGLAHKGDVVVMVSGALVPSGTTNTASVHVL* %%%%%%%%%%%%%%%%%%%%%% end of alignment.ali %%%%%%%%%%%%%%%%%

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Conda package not working on windows (32 bit?) and python 3.5
by Ajasja Ljubetič 16 Sep '16

16 Sep '16

Hi! If I install modeller via conda: export KEY_MODELLER=XXXX conda install -c salilab modeller Then I get an error when importing modeller Python 3.5.2 |Anaconda 4.1.1 (32-bit)| (default, Jul 5 2016, 11:45:57) [MSC v.1900 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> import modeller Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Anaconda3\Library\modeller\modlib\modeller\__init__.py", line 93, in <module> import _modeller ImportError: DLL load failed: The specified module could not be found. >>> I tried copying around the file _modeller.pyd to folders in the PATH, but that did not solve the problem. The installed version is modeller 9.17 py35_1 salilab But if I install from the stand-alone installer everything works fine. Best, Ajasja

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First time running modeller, multiple errors
by Maryam Faridounnia 27 Aug '16

27 Aug '16

Dear Modeller team, I'm trying to get the format right for a multiple alignment, I did in HHpred server to use as input to modeller. I don't understand the error messages. I don't know if the errors are related to the PDB file since I have not changed anything in original PDB unless the extension. I would appreciate very much your advice. Thanks in advance for your help! Maryam ***The input python script: # Comparative 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'] a = automodel(env, alnfile = 'XPF_nuc.ali', # alignment filename knowns = '1j23','4bxo', # codes of the templates sequence = 'XPF') # code of the target 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 the actual comparative modeling ***The run command: mod9.11 model_test1.py ***atom_files: $ ls -l total 952 -rw-r--r-- 1 mfaridou users 121905 Aug 25 23:07 1j23.atm -rw-r--r-- 1 mfaridou users 121905 Aug 25 23:08 1j23.pdb -rw-r--r-- 1 mfaridou users 362313 Aug 25 23:08 4bxo.atm -rw-r--r-- 1 mfaridou users 362313 Aug 25 23:08 4bxo.pdb *** input file XPF_nuc.ali >P1;1j23 structurex:1j23:5:A:125:A:HEFnuclease:Pyrococcus furiosus:1.78:0.248 QEGVKVVVDSRELRSEVVKRLKLLGVKLEVKTLDVGDYIISEDVAIERKSANDLIQSIIDGGLFDQVKRLKEAYSRPIMIVEGS-LY---------GIRNVHPNAIRGAIAAVTVDF-GVPIIFSSTPEETAQYI* >P1;4bxo structurex:4bxo:1:A:151:A:FANCMprotein:Homo sapiens:2.15:0.255 GSGSRPHLAGTHTSLRLPQEGKGTCILVGGHEITSGLEVISSLRAIHGLQVEVCPLNGCDYIVSNRMVVERRSQSEMLNSVNKNKFIEQIQHLQSMFERICVIVEKDREKTGD------TSRMFRRTKSYDSLLTTLIGA-GIRILFSSCQEETADLL* >P1;XPF sequence:XPF:.:.:156:.::0.00: 0.00 RGTASADVSTDTRKAGGQEQNGTQQSIVVDMREFRSELPSLIHRRGIDIEPVTLEVGDYILTPEMCVERKSISDLIGSLNNGRLYSQCISMSRYYKRPVLLIEFDPSKPFSLTSRGALFQEISSNDISSKLTLLTLHFPRLRILWCPSPHATAELF* ***The runtime messages: 'import site' failed; use -v for traceback Traceback (most recent call last): File "model_test1.py", line 18, in ? a.make() # do the actual comparative modeling File "/home/software/software//modeller_v9.11/modlib/modeller/automodel/automodel.py", line 96, in make self.homcsr(exit_stage) File "/home/software/software//modeller_v9.11/modlib/modeller/automodel/automodel.py", line 423, in homcsr self.check_alignment(aln) File "/home/software/software//modeller_v9.11/modlib/modeller/automodel/automodel.py", line 405, in check_alignment aln.check() File "/home/software/software//modeller_v9.11/modlib/modeller/alignment.py", line 197, in check self.check_structure_structure(io=io) File "/home/software/software//modeller_v9.11/modlib/modeller/alignment.py", line 206, in check_structure_structure return f(self.modpt, io.modpt, self.env.libs.modpt, eqvdst) _modeller.SequenceMismatchError: read_te_291E> Sequence difference between alignment and pdb :

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Best practices in point mutation
by Lucas 26 Jul '16

26 Jul '16

I've noticed Sali and Fiser have a 2007 article on point mutations (Modeling mutations in protein structures, Protein Science 16:2030-2041). I couldn't find the modeller script for that protocol, though. Is it available somewhere? Would the protocol at the wiki ( http://salilab.org/modeller/wiki/Mutate%20model ) be the most reliable approach these days? Cheers, Lucas

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How to write a dope score for selected region of optimized atoms along with whole model?
by Mahesh Velusamy 25 Jul '16

25 Jul '16

Dear All, I have tried generate a models for single point mutation along with own optimization and refinement criteria on the selected region of atoms around the mutated residue which works fine but the problem is in dope score. I thing modeler generate DOPE score only for selected region of optimized atoms but I want to write the dope score for optimized region of atoms along with whole structure. Is it possible to do the same? Here is my script used for select part of region from model and perform optimization: class MyModel(automodel): def select_atoms(self): s = selection(self.atoms['CA:'+"275"+':'+"A"].select_sphere(5)).by_residue() self.restraints.unpick_all() self.restraints.pick(s) s.energy() s.randomize_xyz(deviation=4) return s def optimize(s, sched): for step in sched: step.optimize(s, max_iterations=200, min_atom_shift=0.001) refine(s) cg = conjugate_gradients() cg.optimize(s, max_iterations=200, min_atom_shift=0.001) def refine(self,s,actions): md = molecular_dynamics(cap_atom_shift=0.39, md_time_step=4.0, md_return='FINAL') init_vel = True for (its, equil, temps) in ((200, 20, (150.0, 250.0, 400.0, 700.0, 1000.0)), (200, 600, (1000.0, 800.0, 600.0, 500.0, 400.0, 300.0))): for temp in temps: md.optimize(s, init_velocities=init_vel, temperature=temp, max_iterations=its, equilibrate=equil) init_vel = False Thanking you in advance With Regards Mahesh V

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loop modeling and hydrogen atoms
by flavio seixas 25 Jul '16

25 Jul '16

Hi, I generated a model with all hydrogens using allhmodel. Now I want to refine a loop in this model using the script below. However, the hydrogens are being removed in the generated models. Would you help me? from modeller import * from modeller.automodel import * log.verbose() env = environ() env.io.atom_files_directory = ['.', '../atom_files'] # Read in HETATM records from template PDBs env.io.hetatm = True env.io.water = True env.io.hydrogen = True # Create a new class based on 'loopmodel' so that we can redefine # select_loop_atoms class MyLoop(loopmodel): # This routine picks the residues to be refined by loop modeling def select_loop_atoms(self): # Two residue ranges (both will be refined simultaneously) return selection(self.residue_range('399:', '406:')) m = MyLoop(env, inimodel='Model172.pdb', sequence='protein', loop_assess_methods=(assess.DOPE, assess.GA341)) m.loop.starting_model= 1 # index of the first loop model m.loop.ending_model = 200 # index of the last loop model m.loop.md_level = refine.very_fast # loop refinement method m.make() # Get a list of all successfully built models from m.loop.outputs ok_models = filter(lambda x: x['failure'] is None, m.loop.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 1: %s (DOPE score %.3f)" % (m['name'], m[key]) m = ok_models[1] print "Top model 2: %s (DOPE score %.3f)" % (m['name'], m[key]) m = ok_models[2] print "Top model 3: %s (DOPE score %.3f)" % (m['name'], m[key]) m = ok_models[3] print "Top model 4: %s (DOPE score %.3f)" % (m['name'], m[key]) m = ok_models[4] print "Top model 5: %s (DOPE score %.3f)" % (m['name'], m[key]) ------------------------------------- Flavio Augusto Vicente Seixas Laboratory of Structural Biochemistry Department of Biochemistry Universidade Estadual de Maringá, PR, Brazil http://www.uem.br

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Why there is a DOPE score decreases when I optimize the selected atoms of automodel?
by Mahesh Velusamy 15 Jul '16

15 Jul '16

Dear All, I have tried generate a models for single point mutation along with optimization and refinement on the selected region of atoms but I am getting bad DOPE score(lesser negative value -11k) for optimized models compared to unoptimized automodels DOPE score(-36K). Also, I observed that DOPE score get increase when I increase the number of atoms for optimization but at same time this leads to bad models. May I know what is the reason for this issue? because when I do the same thing with mutate_model.py I got the same DOPE score as automodels. Here, I am giving the MyModel class where I define my own optimization,refinement and restraints criteria (same as mutate_model.py): class MyModel(automodel): def select_atoms(self): # a non-bonded pair has to have at least as many selected atoms self.env.edat.nonbonded_sel_atoms = 1 sched = autosched.loop.make_for_model(self) s=selection( self.atoms['CA:'+'102'+':'+'A'].select_sphere(20)).by_residue() self.restraints.unpick_all() self.restraints.pick(s) s.energy() #s.randomize_xyz(deviation=-4) s.energy() return s def optimize(s, sched): # conjugate gradient for step in sched: step.optimize(s, max_iterations=200, min_atom_shift=0.001) # md refine(s) cg = conjugate_gradients() cg.optimize(s, max_iterations=200, min_atom_shift=0.001) def refine(self,atmsel,actions): # at T=1000, max_atom_shift for 4fs is cca 0.15 A. md = molecular_dynamics(cap_atom_shift=0.39, md_time_step=4.0, md_return='FINAL') init_vel = True for (its, equil, temps) in ((200, 20, (150.0, 250.0, 400.0, 700.0, 1000.0)), (200, 600, (1000.0, 800.0, 600.0, 500.0, 400.0, 300.0))): for temp in temps: md.optimize(atmsel, init_velocities=init_vel, temperature=temp, max_iterations=its, equilibrate=equil) init_vel = False def special_restraints(self, aln): rsr = self.restraints rsr.clear() s = selection(self) for typ in ('stereo', 'phi-psi_binormal'): rsr.make(s, restraint_type=typ, aln=aln, spline_on_site=True) for typ in ('omega', 'chi1', 'chi2', 'chi3', 'chi4'): rsr.make(s, restraint_type=typ + '_dihedral', spline_range=4.0, spline_dx=0.3, spline_min_points=5, aln=aln, spline_on_site=True) Thanking you in advance With Regards Mahesh V

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parallel modeling _automodel class
by Sajad Ahrari 15 Jul '16

15 Jul '16

Dear users, I am trying to do a parallel job for protein loop refinement by MODELLER9.17. I prepared the scripts as follows and provided all the input files in my working directory and I am running MODELLER through python2.7 (by typing: "python ModellerModelling.py" ). Although I encounter no error through the modeling process, but I doubt if I am doing a parallel job since when I check my workstation CPU usage, only one CPU is engaged (the usage percent does not exceed 100%). Could you please help me know what am I missing? Best regards,Sajjad ModellerModeling.py: # --- UCSF Chimera Copyright --- # Copyright (c) 2000 Regents of the University of California. # All rights reserved. This software provided pursuant to a # license agreement containing restrictions on its disclosure, # duplication and use. This notice must be embedded in or # attached to all copies, including partial copies, of the # software or any revisions or derivations thereof. # --- UCSF Chimera Copyright --- # This script is generated by the Modeller Dialog in Chimera, # incorporating the settings from the user interface. User can revise # this script and submit the modified one into the Advanced Option panel. # Import the Modeller module from modeller import * from modeller.automodel import * from modeller.parallel import * from mymodel import MyModel log.minimal() a.use_parallel_job(j) # Use the job for model building # Use 16 CPUs in a parallel job on this machine j = job() j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) results = j.run_all_tasks() print("Got model resolution: " + str(results)) mymodel.py: from modeller import * from modeller.automodel import * # ---------------------- namelist.dat -------------------------------- # A "namelist.dat" file contains the file names, which was output from # the Modeller dialog in Chimera based on user's selection. # The first line it the name of the target sequence, the remaining # lines are name of the template structures namelist = open( 'namelist.dat', 'r' ).read().split('\n') tarSeq = namelist[0] template = tuple( [ x.strip() for x in namelist[1:] if x != '' ] ) # ---------------------- namelist.dat -------------------------------- # This instructs Modeller to display all log output. log.verbose() # create a new Modeller environment env = environ() # Directory of atom/PDB/structure files. It is a relative path, inside # a temp folder generated by Chimera. User can modify it and add their # absolute path containing the structure files. env.io.atom_files_directory = ['.', './template_struc'] # Read in HETATM records from template PDBs env.io.hetatm = True # Read in water molecules from template PDBs env.io.water = True # create a subclass of automodel or loopmodel, MyModel. # user can further modify the MyModel class, to override certain routine. class MyModel(loopmodel): def select_loop_atoms(self): from modeller import selection return selection( self.residue_range('256', '309')) def select_atoms(self): from modeller import selection return selection( self.residue_range('256', '309')) def customised_function(self): pass #code overrides the special_restraints method #def special_restraints(self, aln): #code overrides the special_patches method. # e.g. to include the addtional disulfides. #def special_patches(self, aln): a = MyModel(env, sequence = tarSeq, # alignment file with template codes and target sequence alnfile = 'alignment.ali', # name of initial PDB template knowns = template[0]) # one fixed model to base loops on a.starting_model = 1 a.ending_model = 1 # 1 loop models loopRefinement = True a.loop.starting_model = 1 a.loop.ending_model = 1000 a.loop.assess_methods=(assess.DOPE, assess.GA341, assess.normalized_dope) # Assesses the quality of models using # the DOPE (Discrete Optimized Protein Energy) method (Shen & Sali 2006) # and the GA341 method (Melo et al 2002, John & Sali 2003) a.assess_methods = (assess.GA341, assess.normalized_dope) # ------------------------- build all models -------------------------- a.make() # ---------- Accesing output data after modeling is complete ---------- # Get a list of all successfully built models from a.outputs if loopRefinement: ok_models = filter(lambda x: x['failure'] is None, a.loop.outputs) else: ok_models = filter(lambda x: x['failure'] is None, a.outputs) # Rank the models by index number #key = 'num' #ok_models.sort(lambda a,b: cmp(a[key], b[key])) def numSort(a, b, key="num"): return cmp(a[key], b[key]) ok_models.sort(numSort) # Output the list of ok_models to file ok_models.dat fMoutput = open('ok_models.dat', 'w') fMoutput.write('File name of aligned model\t GA341\t zDOPE \n') for m in ok_models: results = '%s\t' % m['name'] results += '%.5f\t' % m['GA341 score'][0] results += '%.5f\n' % m['Normalized DOPE score'] fMoutput.write( results ) fMoutput.close()

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