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