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Nucleic Acids Research Advance Access published online on May 21, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm341
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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M4T: a comparative protein structure modeling server

Narcis Fernandez-Fuentes, Carlos J. Madrid-Aliste, Brajesh Kumar Rai, J. Eduardo Fajardo and András Fiser*

Department of Biochemistry and Seaver Foundation Center for Bioinformatics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA

*To whom correspondence should be addressed. Tel: +1-718-430-3233; Fax: +1-718-430-856; Email: andras{at}fiserlab.org

Received January 23, 2007. Revised April 16, 2007. Accepted April 22, 2007.

Multiple Mapping Method with Multiple Templates (M4T) (http://www.fiserlab.org/servers/m4t) is a fully automated comparative protein structure modeling server. The novelty of M4T resides in two of its major modules, Multiple Templates (MT) and Multiple Mapping Method (MMM). The MT module of M4T selects and optimally combines the sequences of multiple template structures through an iterative clustering approach that takes into account the ‘unique’ contribution of each template, its sequence similarity to other template sequences and to the target sequences, and the quality of its experimental resolution. MMM module is a sequence-to-structure alignment method that is aimed at improving the alignment accuracy, especially at lower sequence identity levels. The current implementation of MMM takes inputs from three profile-to-profile-based alignment methods and iteratively compares and ranks alternatively aligned regions according to their fit in the structural environment of the template structure. The performance of M4T was benchmarked on CASP6 comparative modeling target sequences and on a larger independent test set and showed a favorable performance to current state-of-the-art methods.


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