Workflow homology modelling glucocerebrosidase

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Detailed workflow of the different homology modelling approaches for glucocerebrosidase. Return to overview.

MODELLER

Preparation of the Alignment File

  1. Save target protein sequence in PIR-format: TARGET.pir
  2. Save PDB-file of template sequence: TEMPLATE.pdb
    If PDB-file consists of several chains: split pdb file with the help of splitpdb (note that minor changes are needed, so that ATOM coordinates get listed in the resulting PDB-file instead of HETATOMS).
  3. Run the following Python script with command 'mod9.9 align.py' to create a target-template alignment in PIR-format:

log.verbose()
env = environ()
aln = alignment(env)
mdl= model(env, file='TEMPLATE')
aln.append_model(mdl, align_codes='TEMPLATE')
aln.append(file='TARGET.pir', align_codes=('TARGET'))
aln.align(gap_penalties_1d=(-600,-400))
aln.write(file='TARGET_TEMPLATE.ali', alignment_format='PIR')
aln.write(file='TARGET_TEMPLATE.pap', alignment_format='PAP')

Modelling of the Target Structure

  1. Run the following Python script with command 'mod9.9 model.py' to model the structure of the target sequence:
    Note that all files (alignment- and structure file) must be in the same folder

from modeller.automodel import *
log.verbose()
env = environ()
env.io.atom_files_directory = 
a = automodel (env, alnfile = 'TARGET_TEMPLATE.ali', knowns = 'TEMPLATE', sequence = 'TARGET')
a.starting_model = 1
a.ending_model = 1
a.make()

I-TASSER

I-TASSER provides the possibility to exclude homologous structures with a certain sequence identity cut-off, which was used in this analysis as well.

SWISS-MODEL

Automated Mode

The automated mode should only be used, if target and template share more than 50% of sequence identity.

Alignment Mode

To create the Alignments needed as input, the tool ClustalW2 was used with standard settings. Additionally the Alignment created with MODELLER was used for 2WNW