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Bioinformatics Advance Access published online on December 13, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti828
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received March 9, 2005
Revised November 2, 2005
Accepted December 8, 2005

Article

Improved pairwise alignments of proteins in the Twilight Zone using local structure predictions

Yao-ming Huang 1 and Christopher Bystroff 1 *

1 Center for Bioinformatics, Dept of Biology, Rensselaer Polytechnic Institute, Troy, New York, 12180, USA

* To whom correspondence should be addressed.
Christopher Bystroff, E-mail: bystrc{at}rpi.edu


  Abstract

Motivation: In recent years, advances have been made in the ability of computational methods to discriminate between homologous and non-homologous proteins in the "Twilight Zone" of sequence similarity, where the percent sequence identity is a poor indicator of homology. To make these predictions more valuable to the protein modeler, they must be accompanied by accurate alignments. Pairwise sequence alignments are inferences of orthologous relationships between sequence positions. Evolutionary distance is traditionally modeled using global amino acid substitution matrices. But real differences in the likelihood of substitutions may exist for different structural contexts within proteins, since structural context contributes to the selective pressure.

Results: HMMSUM (HMMSTR-based SUbstitution Matrices) is a new model for structural context-based amino acid substitution probabilities consisting of a set of 281 matrices, each for a different sequence-structure context. HMMSUM does not require the structure of the protein to be known. Instead, predictions of local structure are made using HMMSTR, a hidden Markov model for local structure. Alignments using the HMMSUM matrices compare favorably to alignments carried out using the BLOSUM matrices or structure-based substitution matrices SDM and HSDM when validated against remote homolog alignments from BAliBASE. HMMSUM has been implemented using local Dynamic Programming and with the Bayesian Adaptive alignment method.

Availability: Matrices, source codes and programs are available at http://www.bioinfo.rpi.edu/~bystrc/downloads.html.


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