Difference between revisions of "Homology Modeling of ARS A"
Line 41: | Line 41: | ||
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. |
||
+ | |||
+ | {| border="1" style="text-align:center; border-spacing:0;" |
||
+ | | ''' 1P49 ''' |
||
+ | | ''' 2VQR ''' |
||
+ | | ''' 1FSU ''' |
||
+ | | ''' 3ED4 ''' |
||
+ | |- |
||
+ | | [[File:ARSA_POODLE_small.png | frame | center | thumb | plot of POODLE-output showing the probability of being disordered along the sequence]] |
||
+ | | [[File:ARSA_POODLE_small.png | frame | center | thumb | plot of POODLE-output showing the probability of being disordered along the sequence]] |
||
+ | | [[File:ARSA_POODLE_small.png | frame | center |thumb | plot of POODLE-output showing the probability of being disordered along the sequence]] |
||
+ | | [[File:ARSA_POODLE_small.png | frame | center | thumb | plot of POODLE-output showing the probability of being disordered along the sequence]] |
||
+ | |- |
||
+ | |} |
Revision as of 17:19, 7 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 | no | 20.3% |
3ED4 | 32.0% | Escherichia coli | Arylsulfatase | yes | 27.7% |
Our potential tmeplates, identified by the database searches contain all homologs with known structure, regarding to HSSP.
In order to predict the structure, modeller needs pairwise sequence alignments in PIR format. 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 |