Including disulfide bridges

If there is an equivalent disulfide bridge in any of the templates aligned with the target, automodel will automatically generate appropriate disulfide bond restraints^2.2 for you (by using the model.patch_ss_templates() command).

Explicit manual restraints can be added by the model.patch() command using the CHARMM topology file DISU patching residue. You must redefine the automodel.special_patches() routine to add these or other patches.

It is better to use model.patch_ss_templates() rather than model.patch() where possible because the dihedral angles are restrained more precisely by using the templates than by using the general rules of stereochemistry.

Some CHARMM parameter files have a multiple dihedral entry for the disulfide dihedral angle $\chi _3$ that consists of three individual entries with periodicities of 1, 2 and 3. This is why you see three feature restraints for a single disulfide in the output of the selection.energy() command.

Example: examples/automodel/model-disulfide.py

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

# Redefine the special_patches routine to include the additional disulfides
# (this routine is empty by default):
class mymodel(automodel):
    def special_patches(self, aln):
        # A disulfide between residues 8 and 45:
        self.patch(residue_type='DISU', residues=(self.residues['8'],
                                                  self.residues['45']))

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'

a = mymodel(env,
            alnfile  = 'alignment.ali',     # alignment filename
            knowns   = '5fd1',              # codes of the templates
            sequence = '1fdx')              # code of the target
a.starting_model= 1                 # index of the first model 
a.ending_model  = 1                 # index of the last model
                                    # (determines how many models to calculate)
a.make()                            # do the actual homology modeling