Difference between revisions of "Homology Modeling of ARS A"

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(Single template modelling)
(Single template modelling)
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Further on, we visualised the models using pymol. We load both structures into the program and performed a structural alignment to superimpose and compare them visually.
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Further on, we visualised the models using pymol. We load both structures into the program and performed a structural alignment to superimpose and compare them visually. The pymol commands and the images are shown below:
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<code>
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align 1AUK, MODEL
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hide all
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show cartoon
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# select color of modelled structure via graphical interface
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ray
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cmd.png("1AUK.1P49.png")
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</code>
   
 
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Revision as of 10:07, 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.

Next, we calculated RMSD and TM scores of the models to get a first impression on how much the models deviate from the original structure. The results are depicted in the table below.


Further on, we visualised the models using pymol. We load both structures into the program and performed a structural alignment to superimpose and compare them visually. The pymol commands and the images are shown below:


align 1AUK, MODEL
hide all
show cartoon
# select color of modelled structure via graphical interface
ray
cmd.png("1AUK.1P49.png")

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