Hi all, I created 5 models with a modified automodel script: a = automodel(env, alnfile = "bcs1h.fasta", knowns = ("1E32m", "1IXZm", "1IY1m", "1LV7m", "1S3Sm", "2CE7m", "2QZ4m"), sequence = ("bcs1h"), assess_methods=(assess.DOPE)) a.starting_model = 1 a.ending_model = 5 a.library_schedule = autosched.slow a.max_var_iterations = 300 a.md_level= refine.slow a.repeat_optimization = 2 a.max_molpdf = 1e6 a.make()
These models have their OBJECTIVE FUNCTION around 11,000 . I made some DOPE plots to check for poorly defined regions. I detected some loops where the DOPE score was higher. I decided to further modelize these loops with loop_model as shown in the tutorial selecting residues having DOPE score significatively higher than the average DOPE for the entire structure. Here is the script:
class MyLoop(loopmodel): def select_loop_atoms(self): return selection( self.residue_range(1, 26), self.residue_range(75,84), self.residue_range(89,97), self.residue_range(102,122), self.residue_range(183,204), self.residue_range(230,238)) for i in pdb_names: m = MyLoop (env, inimodel=("bcs1h.B9999000%s.pdb" %(i)), sequence=("bcsh1.%s" %(i)), library_schedule = autosched.slow, loop_assess_methods=(assess.DOPE, assess.GA341)) m.loop.starting_model= 1 m.loop.ending_model = 5 m.loop.md_level = refine.slow_large m.loop.max_var_iterations = 300 m.loop.library_schedule = autosched.slow m.make()
Quite surprisingly, the OBJECTIVE function of the 25 produced loop models varies from 2000 to 7000 and the DOPE plots of the different loop models is not as good as the first models!! Is there something wrong in the script or do I need to change and optimize some other parameters?
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Dr. Gilles Truan
Centre de Génétique Moléculaire, CNRS
1 Av. de la Terrasse, 91198 Gif-sur-Yvette, France
Phone: 33-1-69 82 36 65, Fax: 33-1-69 82 36 82,
http://www.cgm.cnrs-gif.fr/pompon/index.html Lab Web Site
mailto:gtruan@cgm.cnrs-gif.fr Email
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