Homology Modeling of ARS A
HHpred
We used the webserver and
Modeller
Proteins used as templates
We identified the following proteins (see Alignment TASK) as potential targets for homology modeling:used the following
SeqIdentifier | Seq Identity (from TASK 2) | source | Protein function | True homolog (HSSP) | Seq Identity (pairw. ali.) |
---|---|---|---|---|---|
1P49 | 39.0% | Homo Sapiens | Steryl-Sulfatase | yes | 31.9% |
1FSU | 28.0% | Homo Sapiens | Arylsulfatase B | yes | 26.5% |
2VQR | 20.0% | Rhizobium leguminosarum | Sulfatase | Monoester Hydrolase | 20.3% |
3ED4 | 32.0% | Escherichia coli | Arylsulfatase | yes | 27.7% |
Our potential templates, identified by the database searches contain all homologs with known structure, regarding to HSSP.
Single template modelling
In order to predict the structure using a single template structure, modeller needs pairwise sequence alignments in PIR format. Modeller provides two different methods to calculate pairwise sequence alignments. alignment.malign()
uses classical dynamic programming to align two sequences. alignment.alig2dn()
also uses a dynamic programming approach, but includes structural information to optimize the alignment (e.g. tries to place gaps outside of secondary structure elements). We applied both alignment methods and created eight pairwise sequnece alignments of the above templates with the target. The script used for this purpose is shown below:
from modeller import *
env = environ()
aln = alignment(env)
mdl = model(env, file='template_name', model_segment=('FIRST:@', 'END:'))
aln.append_model(mdl, align_codes='template_name', atom_files='template_name')
aln.append(file='1AUK.pir', align_codes='target_name')
aln.align2d()
aln.check()
aln.write(file='target-template-2d.ali', alignment_format='PIR')
aln.malign()
aln.check()
aln.write(file='target-template.ali', alignment_format='PIR')
We wrote a python script (align_all.py) - using the example python script aln_append_model.py - to achieve this. Then we wrote one Python script (using the example script model-default.py) per protein to build the model, using the alignments and the pdb structures.
We executed the following commands:
/apps/modeller9.9/bin/mod9.9 align_all.py
/apps/modeller9.9/bin/mod9.9 predict1p49.py
/apps/modeller9.9/bin/mod9.9 predict2vqr.py
/apps/modeller9.9/bin/mod9.9 predict1fsu.py
/apps/modeller9.9/bin/mod9.9 predict3ed4.py
We modified the paths and filenames in the scripts such that it matched our proteins of interest.
1P49 | 2VQR | 1FSU | 3ED4 |