Difference between revisions of "Homology Modeling of ARS A"
(→Proteins used as templates) |
(→Single template modelling) |
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+ | For these alignments we constructed eight models, using the following script: |
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− | 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. |
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+ | <code> |
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− | We executed the following commands: |
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+ | from modeller import * |
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− | |||
+ | from modeller.automodel import * |
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− | <code> |
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+ | log.verbose() |
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− | /apps/modeller9.9/bin/mod9.9 align_all.py |
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+ | env = environ() |
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− | /apps/modeller9.9/bin/mod9.9 predict1p49.py |
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+ | a = automodel(env, |
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− | /apps/modeller9.9/bin/mod9.9 predict2vqr.py |
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+ | alnfile = '1AUK-1FSU-2d.ali', |
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− | /apps/modeller9.9/bin/mod9.9 predict1fsu.py |
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+ | knowns = '1FSU', |
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− | /apps/modeller9.9/bin/mod9.9 predict3ed4.py |
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+ | sequence = '1AUK', |
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− | </code> |
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+ | assess_methods=(assess.DOPE, assess.GA341)) |
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− | |||
+ | a.starting_model= 1 |
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+ | a.ending_model = 1 |
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+ | a.make() |
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+ | </code> |
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We modified the paths and filenames in the scripts such that it matched our proteins of interest. |
We modified the paths and filenames in the scripts such that it matched our proteins of interest. |
Revision as of 11:01, 9 June 2011
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')
For these alignments we constructed eight models, using the following script:
from modeller import *
from modeller.automodel import *
log.verbose()
env = environ()
a = automodel(env,
alnfile = '1AUK-1FSU-2d.ali',
knowns = '1FSU',
sequence = '1AUK',
assess_methods=(assess.DOPE, assess.GA341))
a.starting_model= 1
a.ending_model = 1
a.make()
We modified the paths and filenames in the scripts such that it matched our proteins of interest.
1P49 | 2VQR | 1FSU | 3ED4 |