>> a = MyLoopModel(env, alnfile=alignment, >> knowns=known_templates, >> assess_methods=(assess.DOPEHR,assess.normalized_dope), >> sequence='target') >> a.starting_model = 1 >> a.ending_model = 2 >> > > This seems to contradict your statement above. Two models is probably not > going to give you sufficient sampling - as John W suggested, you should be > building many more models - perhaps 100. > > That was a test code to explain why I don't get enough models. Of course the numbers I use are much higher.
> a.loop.starting_model = 1 # First loop model >> a.loop.ending_model = 5 # Last loop model >> > > The .IL files are initial (unoptimized) loops, so they are of little > utility. But there should be many more loop (.BL) models generated. The log > file will tell you why a particular model optimization failed. > > Indeed I have 9 messages like this:
target.BL00010001.pdb check_inf__E> Atom 1 has out-of-range coordinates (usually infinity). The objective function can thus not be calculated.
> 2) I 'd like to ask your opinion about the most effective way to find a >> near-native protein conformation in low sequence identity levels. How >> should the parameters shown above be set? I don't care if it's running a >> day or so as long as I get good results. >> > > Build more models - that's the most effective way. > > You mean without loop optimization? From your experience what values should be assigned to the following parameters:
# Normal VTFM model optimization: a.library_schedule = autosched.normal a.max_var_iterations = 200 # 200 by default # Very thorough MD model optimization: a.md_level = refine.slow a.repeat_optimization = 1 a.loop.md_level = refine.slow # Loop model refinement level