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From: <modeller_usage-owner@salilab.org>
Date: Sat, Oct 1, 2022 at 9:32 AM
Subject: Re: [modeller_usage] Homomultimer modeling
To: <mjsubach@alumni.ncsu.edu>

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---------- Forwarded message ----------
From: Joel Subach <mjsubach@alumni.ncsu.edu>
To: "Franceschini Ghilardi, Amanda (BIDMC - Lijun Sun - General Surg SF)" <afrance2@bidmc.harvard.edu>
Cc: "modeller_usage@salilab.org" <modeller_usage@salilab.org>
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Date: Sat, 1 Oct 2022 09:32:25 +0200
Subject: Re: [modeller_usage] Homomultimer modeling

Hello I would first need to be sure that you selected the correct template(s) accordingly if you want please send me your protein heptamer query and the template(s) you selected and how you discovered these templates. (I completed my Doctoral

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Hello I would first need to be sure that you selected the correct template(s) accordingly if you want please send me your protein heptamer query

and the template(s) you selected and how you discovered these templates.  (I completed my Doctoral Dissertation on modeling an unknown heptamer.)

Thanks:)

Joel 🚀

On Fri, Sep 30, 2022 at 11:51 PM Franceschini Ghilardi, Amanda (BIDMC - Lijun Sun - General Surg SF) via modeller_usage <modeller_usage@salilab.org> wrote:

Hi, I am trying to model a heptamer (homomultimer), but the structure generated is wrong because there are many clashes and it looks completely different from the template. How can I solve this issue? Please, see my files and scripts below. 

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Hi,

I am trying to model a heptamer (homomultimer), but the structure generated is wrong because there are many clashes and it looks completely different from the template. How can I solve this issue? Please, see my files and scripts below.

The .ali for the alignment was edited by repeating the sequence seven times and separating each sequence by "/"

>P1;name

Sequence:name::::::::

XXXXX/XXXXX/XXXXX/XXXXX* 

The alignment script

from modeller import *

env = Environ()

aln = Alignment(env)

mdl = Model(env, file='template', model_segment=('FIRST:A','LAST:F'))

aln.append_model(mdl, align_codes='template', atom_files='template.pdb')

aln.append(file='target.ali', align_codes='target')

aln.align2d(max_gap_length=50)

aln.write(file='aligned.ali', alignment_format='PIR')

aln.write(file='aligned.pap', alignment_format='PAP')

The script to build the model

from modeller import *

from modeller.automodel import *

#from modeller import soap_protein_od

log.verbose()

# Override the 'special_restraints' and 'user_after_single_model' methods:

class MyModel(AutoModel):

    def special_restraints(self, aln):

        # Constrain the A and B 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')

        s3 = Selection(self.chains['C']).only_atom_types('CA')

        s4 = Selection(self.chains['D']).only_atom_types('CA')

        s5 = Selection(self.chains['E']).only_atom_types('CA')

        s6 = Selection(self.chains['F']).only_atom_types('CA')

        self.restraints.symmetry.append(Symmetry(s1, s2, 1.0))

    def user_after_single_model(self):

        # Report on symmetry violations greater than 1A after building

        # each model:

        self.restraints.symmetry.report(1.0)

env = Environ()

# directories for input atom files

env.io.atom_files_directory = ['.', '../atom_files']

# Be sure to use 'MyModel' rather than 'AutoModel' here!

a = MyModel(env,

            alnfile  = 'aligned.ali' ,     # alignment filename

            knowns   = 'template',              # codes of the templates

            sequence = 'target',

assess_methods=(assess.DOPE))              # code of the target

a.starting_model= 1                # index of the first model

a.ending_model  = 100                # index of the last model

                                   # (determines how many models to calculate)

a.make()                           # do comparative modeling

# Get a list of all succesfully built models from a.output

ok_models = [x for x in a.outputs if x['failure'] is None]

# Rank the models by DOPE score

key = 'DOPE score'

if sys.version_info[:2] == (2,3):

        ok_models.sort(lambda a,b: cmp(a[key], b[key]))

else:

        ok_models.sort(key=lambda a: a[key])

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