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Bioinformatics Advance Access published online on August 25, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti616
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received March 16, 2005
Revised July 28, 2005
Accepted August 4, 2005

Article

Pairwise alignment incorporating dipeptide covariation

Gavin E. Crooks 1* {dagger}, Richard E. Green 2, and Steven E. Brenner 2

1 Dept. of Plant and Microbial Biology, 111 Koshland Hall #3102, USA; Current Address: Physical Biosciences Division, Lawrence Berkeley Natl. Lab, Berkeley, CA 94720
2 Dept. of Plant and Microbial Biology, 111 Koshland Hall #3102, USA; Dept. of Molecular and Cell Biology, University of California, Berkeley, CA 94720-3102, USA

* To whom correspondence should be addressed.
Gavin E. Crooks, E-mail: gec{at}compbio.berkeley.edu


  Abstract

Motivation: Standard algorithms for pairwise protein sequence alignment make the simplifying assumption that amino acid substitutions at neighboring sites are uncorrelated. This assumption allows implementation of fast algorithms for pairwise sequence alignment, but it ignores information that could conceivably increase the power of remote homolog detection. We examine the validity of this assumption by constructing extended substitution matrixes that encapsulate the observed correlations between neighboring sites, by developing an efficient and rigorous algorithm for pairwise protein sequence alignment that incorporates these local substitution correlations, and by assessing the ability of this algorithm to detect remote homologies.

Results: Our analysis indicates that local correlations between substitutions are not strong on the average. Furthermore, incorporating local substitution correlations into pairwise alignment did not lead to a statistically significant improvement in remote homology detection. Therefore, the standard assumption that individual residues within protein sequences evolve independently of neighboring positions appears to be an efficient and appropriate approximation.

Availability: Sequence data, software, and matrixes are freely available from http://compbio.berkeley.edu/.

{dagger}These authors contributed equally to this work.
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