# --- 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 myloop import MyLoop # ---------------------- 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 -------------------------------- #Run a parallel job on3 CPUs j = job() j.append(local_slave()) j.append(local_slave()) j.append(local_slave()) # 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 a = MyLoop(env, sequence = tarSeq, # alignment file with template codes and target sequence alnfile = 'alignment.ali', # PDB codes of the templates knowns = template, loop_assess_methods = assess.normalized_dope) # one fixed model to base loops on a.starting_model = 1 a.ending_model = 1 #Faster refinement at the cost of not as good MD annealing a.loop.md_level = refine.fast loopRefinement = True a.loop.starting_model = 1 # index of the last model a.loop.ending_model = 10 # 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.normalized_dope) # ------------------------- build all models -------------------------- a.use_parallel_job(j) a.make() # ---------- Accesing output data after modeling is complete ---------- # Get a list of all successfully built models from a.outputs ok_models = [x for x in a.loop.outputs if x['failure'] is None] key = 'Normalized DOPE score' ok_models.sort(key=lambda a: a[key]) # 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 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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# Import the Modeller module from modeller import * from modeller.automodel import * # create a subclass of automodel or loopmodel, MyModel. # user can further modify the MyModel class, to override certain routine. class MyLoop(loopmodel): def select_loop_atoms(self): return selection(self.residue_range('54:','64:'), self.residue_range('77:', '92:')) #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):