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