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Bioinformatics Advance Access published online on November 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti791
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 18, 2005
Revised November 15, 2005
Accepted November 16, 2005

Article

A structure and evolution guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins

I. Mihalek 1 *, I. Res 1, and O. Lichtarge 1

1 Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030

* To whom correspondence should be addressed.
I. Mihalek, E-mail: imihalek{at}bcm.tmc.edu


  Abstract

Motivation: Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces.

Results: Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection, or match the quality of prediction obtained through more elaborate non-structure based methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.


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