Information Technology Reference
In-Depth Information
Table 6 List of web servers for modeling the protein structure by homology modeling or
comparative modeling method along with the webpage URL and the programmes description
S. no.
Name of the web server/group [URL]
Description of the web server/group
1
GENO3D (Combet et al. 2002 )[ http://
geno3d-pbil.ibcp.fr/ ]
A web server which builds the model
based on distance geometry, simulated
annealing and energy minimization
algorithms to build the protein 3D
model
2
M4T (Fernandez-Fuentes et al. 2007 )
[ http://manaslu.aecom.yu.edu/M4T/ ]
A fully automated comparative protein
structure modeling server with two
major modules, Multiple Templates
(MT) and Multiple Mapping Method
(MMM)
3
CPHMODELS 3.2 (Nielsen et al. 2010 )
[ http://www.cbs.dtu.dk/services/
CPHmodels/ ]
Protein modeling is based on pro le-
pro le alignment guided by secondary
structure and exposure predictions
4
3DJIGSAW (Bates et al. 2001 )[ http://
www.bmm.icnet.uk/servers/3djigsaw/ ]
An automated server to build three-
dimensional models for proteins based
on homologues of known structure
5
PUDGE (Norel et al. 2010 ) https://
bhapp.c2b2.columbia.edu/pudge/cgi-
bin/pipe_int.cgi
A server that includes secondary struc-
ture predictions, domains predictions
and disorder prediction to predict the
high quality homology model
6
SWISS-MODEL (Guex and Peitsch
1997 )[ http://swissmodel.expasy.org/
SWISSMODEL.html ]
A fully automated protein structure
homology-modeling server
7
ESYPRED3D (Lambert et al. 2002 )
[ http://www.fundp.ac.be/sciences/
biologie/urbm/bioinfo/esypred/ ]
This server results in good protein
model by using several multiple align-
ment programs by combining, weighing
and screening
4 Hybrid Methods for Protein Tertiary Structure
Prediction
Nowadays, a number of fully automated hybrid methods are designed in order to
perform rapid, completely automated fold recognition on a proteome wide scale.
These hybrid methods club together a number of features such as structural
alignments, solvent accessibility and secondary structure information in order to
produce a protein model with high accuracy. Some such methods are discussed
below.
GenTHREADER (Jones 1999b ) is a fully automated hybrid method for fold
recognition which uses a traditional sequence alignment algorithm to generate
alignments. These generated alignments are thereafter evaluated by a method
derived from threading techniques. The algorithm for GenTHREADER is divided
into three stages: alignment of sequences, calculation of pair potential as well as
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