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[modeller_usage] NaN error model generation Lennard-Jones restraints



Hi,

In order to obtain models with less clashes (and improved stereochemistry) as measured by Molprobity, I followed this thread: http://salilab.org/archives/modeller_usage/2010/msg00382.html

Taking Nick's advice into account, I switched on the dynamic Lennard-Jones restraints with the simple script shown below. Some models are generated, showing improved statistics regarding clashes and stereochemical quality, but many others fail to be generated due to NaN values in the optimization process (see 'yyyy.D00000002' file attached), and shown in the .log file as:
...
yyyy.B99990002.pdb        Obj. func. (nan) exceeded max_molpdf (10000000.000)
...

I have varied many variables (different alignments, targets, using optimization values as in the 'model_changeopt.py' example script...), but the problem remains. The .ini file looks fine, and of course switching off LJ restraints does not yied that error.

I hope someone would help with this issue. Thanks in advance.

Regards,

-- automodel_lj.py --
# Homology modeling by the automodel class
from modeller import *              # Load standard Modeller classes
from modeller.automodel import *    # Load the automodel class

log.verbose()    # request verbose output
env = environ()  # create a new MODELLER environment to build this model in

# directories for input atom files
env.io.atom_files_directory = ['./']
energy_data.dynamic_lennard = True

a = automodel(env,
              alnfile  = 'file.ali',     # alignment filename
              knowns   = 'xxxx',              # codes of the templates
              sequence = 'yyyy', # code of the target
              assess_methods=(assess.DOPE, assess.GA341))            
a.starting_model= 1                 # index of the first model
a.ending_model  = 15                 # index of the last model
                                    # (determines how many models to calculate)
## Very thorough VTFM optimization:
#a.library_schedule = autosched.slow
#a.max_var_iterations = 300
## Very thorough MD optimization:
#a.md_level = refine.very_slow
a.make()                            # do the actual homology modeling


--
David Rodríguez Díaz, PhD Student
Fundación Pública Galega de Medicina Xenómica (SERGAS)
Santiago de Compostela (Spain)
http://webspersoais.usc.es/persoais/david.rodriguez.diaz

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