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()