Difference between revisions of "Homology Modeling of ARS A"

From Bioinformatikpedia
(Proteins used as templates)
(Proteins used as templates)
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Our potential templates, identified by the database searches contain all homologs with known structure, regarding to HSSP.
 
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, modeller needs pairwise sequence alignments in PIR format. Modeller provides two different methods to calculate pairwise sequence alignments.
 
  +
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. <code> alignment.malign() </code> uses classical dynamic programming to align two sequences. <code> alignment.alig2dn() </code> 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:
   
 
<code>
 
<code>
Line 34: Line 35:
 
env = environ()
 
env = environ()
 
aln = alignment(env)
 
aln = alignment(env)
mdl = model(env, file='2VQR', model_segment=('FIRST:@', 'END:'))
+
mdl = model(env, file='template_name', model_segment=('FIRST:@', 'END:'))
aln.append_model(mdl, align_codes='2VQR', atom_files='2VQR')
+
aln.append_model(mdl, align_codes='template_name', atom_files='template_name')
aln.append(file='1AUK.pir', align_codes='1AUK')
+
aln.append(file='1AUK.pir', align_codes='target_name')
 
aln.align2d()
 
aln.align2d()
 
aln.check()
 
aln.check()
aln.write(file='1AUK-2VQR-2d.ali', alignment_format='PIR')
+
aln.write(file='target-template-2d.ali', alignment_format='PIR')
 
aln.malign()
 
aln.malign()
 
aln.check()
 
aln.check()
aln.write(file='1AUK-2VQR.ali', alignment_format='PIR')
+
aln.write(file='target-template.ali', alignment_format='PIR')
 
</code>
 
</code>
   

Revision as of 09:47, 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') 


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
real structure of 1P49 and structure of 1AUK modelled by modeller, visualized in Pymol
real structure of 2VQR and structure of 1AUK modelled by modeller, visualized in Pymol
real structure of 1FSU and structure of 1AUK modelled by modeller, visualized in Pymol
real structure of 3ED4 and structure of 1AUK modelled by modeller, visualized in Pymol