Re: [modeller_usage] structure refinement and loop optimization protocol
To: Thomas Evangelidis <>
Subject: Re: [modeller_usage] structure refinement and loop optimization protocol
From: Modeller Caretaker <>
Date: Wed, 16 Jun 2010 16:07:33 +0200
Cc:
On 6/15/10 3:29 AM, Thomas Evangelidis wrote:
I've read previous posts on the same topic and concluded that it is
better to generate multiple models with moderate refinement and loop
optimization level, rather that a few with very thorough
parameterization.
Indeed.
I have concluded about the optimum alignment after a lot of
experimentation and would like to set up a very effective optimization
process. However I'm not sure about the output files. My code looks like
this:
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.
a.loop.starting_model = 1 # First loop model
a.loop.ending_model = 5 # Last loop model
This should generate 12 models in total - 2 comparative models
(*.B999*.pdb files), and then 5 further loop models for each comparative
model (*.BL*.pdb files).
Which generates the following pdb files:
target.B99990001.pdb target.B99990002.pdb target.BL00040002.pdb
target.IL00000001.pdb target.IL00000002.pdb
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.
I thought the above should perform model refinement twice and write 5
different conformations (loop optimization) for each.
Indeed.
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.