[modeller_usage] loop modelling

[Judy Barnett-Norris <judy_norris@earthlink.net>]

Hello,

I just wanted to ask a few questions...I am modelling loops on a GPCR.  
My current script follows my questions. 

1.   Will I get better sampling by running 20 different runs (with 
different random number seeds) that create 25 models each thus a total 
of 500 models?
2.  Would setting dynamic_coulomb = True and a relative _dielectric = 80 
be the best way to simulate an aqueous environment for the loops?
3.  Is it possible to run the loop optimization with explicit 
hydrogens?  I guess my env.io.hydrogen = True does not actually set up 
the calculation to do explicit hydrogens
but only tells Modeller to read the hydrogens from my pdb file.
4. For the refinement level, I am using md_level = refine.very_slow  for 
what size system is the refine.slow_large used?
5.  Would I get better sampling by setting repeat.optimization greater 
than 1?
Thank you in advance.

Judy Norris


==========================================================================
#Homology modelling by the automodel class

from modeller.automodel import *    # Load the automodel class

log.verbose()

env = environ(rand_seed=-32601)
env.io.atom_files_directory = './:../atom_files'
env.edat.dynamic_coulomb = True
env.edat.relative_dielectric = 80
env.io.hydrogen = True

class myloop(loopmodel):
   def select_loop_atoms(self):
        stat = 'INITIALIZE'
        for segs in (('1:', '15:'), ('49:', '54:'),
               ('84:', '89:'),('124:','133:'),
               ('226:','233:')):
           self.pick_atoms(selection_segment=segs,
                    selection_search='segment',
                    pick_atoms_set=1,
                    res_types='all',
                    atom_types='all',
                    selection_from='all',
                    selection_status=stat)
           stat = 'ADD'

m = myloop(env,
             inimodel  = 'protein_lp01.B99990001.pdb',     # alignment 
filename
             sequence = 'protein_01')              # code of the target
m.loop.starting_model= 1                 # index of the first model
m.loop.ending_model  = 25                 # index of the last model
                                   # (determines how many models to 
calculate)
m.loop.md_level = refine.very_slow                   # No refinement of 
model

m.make()                            # do homology modelling

=====================================================================