# Loop refinement of an existing model
from modeller import *
from modeller.automodel import *

j = job(modeller_path=os.path.join(modellerPath, "/usr/lib/modeller9.11/bin/modslave.py")
j.append(local_slave()) # 1 Processor
j.append(local_slave()) # 2 Processors
j.append(local_slave()) # 3 Processors
j.append(local_slave()) # 4 Processors
j.append(local_slave()) # 5 Processors
j.append(local_slave()) # 6 Processors
j.append(local_slave()) # 7 Processors
j.append(local_slave()) # 8 Processors
j.append(local_slave()) # 9 Processors
j.append(local_slave()) # 10 Processors             

env = environ()

# directories for input atom files
env.io.atom_files_directory = ['.', '../atom_files']

# Create a new class based on 'loopmodel' so that we can redefine
# select_loop_atoms (necessary)
class MyLoop(loopmodel):
	# This routine picks the residues to be refined by loop modeling
	def select_loop_atoms(self):
	    # One loop from residue 160 to 172 inclusive
	    return selection(self.residue_range('160:', '172:'))

m = MyLoop(env,
	   inimodel='Q9Y478.B99990001', 
	   sequence='Q9Y478_Hbeta1',
           assess_methods=(assess.DOPE, assess.GA341))

m.loop.starting_model = 1              # index of the first loop model
m.loop.ending_model   = 100              # index of the last loop model

# Very thorough VTFM optimization:
m.library_schedule = autosched.fast
m.max_var_iterations = 10000

# Thorough MD optimization:
m.md_level = refine.slow

# Repeat the whole cycle 2 times and do not stop unless obj.func. > 1E6
m.repeat_optimization = 2
m.max_molpdf = 1e6

m.use_parallel_job(j)       # Use the job for model building
m.make()                    # do homology modeling