Statistical potential‐based amino acid similarity matrices for aligning distantly related protein sequences

Yen Hock Tan; He Huang; Daisuke Kihara

doi:10.1002/prot.21020

journal article Jun 23, 2006

Statistical potential‐based amino acid similarity matrices for aligning distantly related protein sequences

Yen Hock Tan He Huang

Daisuke Kihara

Proteins: Structure, Function, and Bioinformatics Vol. 64 No. 3 pp. 587-600 · Wiley

View at Publisher Save 10.1002/prot.21020

Abstract

AbstractAligning distantly related protein sequences is a long‐standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile–profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile–profile alignments. Here we have developed novel amino acid similarity matrices from knowledge‐based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential‐based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential‐based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential‐based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space. Proteins 2006. © 2006 Wiley‐Liss, Inc.

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References

Details

Published: Jun 23, 2006
Vol/Issue: 64(3)
Pages: 587-600
License: View

Authors

Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States

D

Daisuke Kihara

Purdue University West Lafayette

Cite This Article

Yen Hock Tan, He Huang, Daisuke Kihara (2006). Statistical potential‐based amino acid similarity matrices for aligning distantly related protein sequences. Proteins: Structure, Function, and Bioinformatics, 64(3), 587-600. https://doi.org/10.1002/prot.21020

Statistical potential‐based amino acid similarity matrices for aligning distantly related protein sequences

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