Hi Ben,

I try to make a distance restraint between two atoms (protein and cofator) in a homodimer protein.

However, I face another error:

[flavios@localhost]$ mod9.13 model-mult-simmetry-bond.py
Could not find platform independent libraries <prefix>
Could not find platform dependent libraries <exec_prefix>
Consider setting $PYTHONHOME to <prefix>[:<exec_prefix>]
'import site' failed; use -v for traceback
Traceback (most recent call last):
File "model-mult-simmetry-bond.py", line 40, in ?
    a.make()
File "/usr/lib/modeller9.13/modlib/modeller/automodel/automodel.py", line 108, in make
    self.multiple_models(atmsel)
File "/usr/lib/modeller9.13/modlib/modeller/automodel/automodel.py", line 212, in multiple_models
    self.outputs.append(self.single_model(atmsel, num))
File "/usr/lib/modeller9.13/modlib/modeller/automodel/automodel.py", line 308, in single_model
    self.model_analysis(atmsel, filename, out, num)
File "/usr/lib/modeller9.13/modlib/modeller/automodel/automodel.py", line 350, in model_analysis
    self.user_after_single_model()
File "model-mult-simmetry-bond.py", line 34, in user_after_single_model
    rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NZ:38'], at['C4:382']), mean=1.5, stdev=0.1))
NameError: global name 'rsr' is not defined

I am certain that it must be an script error, but I do not know how to fix (I am not familiar with python script).
My script is:

# Homology modeling with single template
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 = ['.', '../atom_files']

# Read in HETATM records from template PDBs
env.io.hetatm = True
env.io.water = True

class MyModel(automodel):
    def special_restraints(self, aln):
        rsr = self.restraints
        at = self.atoms
        # Constrain the A, B, C and D chains to be identical (but only restrain
        # the C-alpha atoms, to reduce the number of interatomic distances
        # that need to be calculated):
        s1 = selection(self.chains['A']).only_atom_types('CA')
        s2 = selection(self.chains['B']).only_atom_types('CA')
        self.restraints.symmetry.append(symmetry(s1, s2, 1))

    def user_after_single_model(self):
        # Report on symmetry violations greater than 1A after building
        # each model:
        self.restraints.symmetry.report(1.0)

       #Restrain between LYS and PLP (st. dev.=0.1)
       #Use a harmonic potential and X-Y distance group.
        rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NZ:38'], at['C4:382']), mean=1.5, stdev=0.1))
        rsr.add(forms.gaussian(group=physical.xy_distance, feature=features.distance(at['NZ:424'], at['C4:768']), mean=1.5, stdev=0.1))

a=MyModel(env, alnfile='AlRacem-mult.ali', knowns=('4A3Q', '1BD0'), sequence='AlRacem', assess_methods=(assess.DOPE, assess.GA341))
a.starting_model = 1
a.ending_model = 10
a.make()

# Get a list of all successfully built models from a.outputs
ok_models = filter(lambda x: x['failure'] is None, a.outputs)

# Rank the models by DOPE score
key = 'DOPE score'
ok_models.sort(lambda a,b: cmp(a[key], b[key]))

# Get top model
m = ok_models[0]

print "Top model 1: %s (DOPE score %.3f)" % (m['name'], m[key])
m = ok_models[1]

print "Top model 2: %s (DOPE score %.3f)" % (m['name'], m[key])
m = ok_models[2]

print "Top model 3: %s (DOPE score %.3f)" % (m['name'], m[key])
m = ok_models[3]

print "Top model 4: %s (DOPE score %.3f)" % (m['name'], m[key])
m = ok_models[4]

print "Top model 5: %s (DOPE score %.3f)" % (m['name'], m[key])

-------------------------------------
Flavio Augusto Vicente Seixas
Laboratory of Structural Biochemistry
Department of Biochemistry
Universidade Estadual de Maringá, PR, Brazil
http://www.uem.br