Protein family comparison using statistical models and predicted structural information

doi:10.1186/1471-2105-5-183

BMC Bioinformatics

Volume 5

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Methodology article

Protein family comparison using statistical models and predicted structural information

Richard Chung and Golan Yona

Department of Computer Science, Cornell University, Ithaca, NY 14850, USA

author email corresponding author email

BMC Bioinformatics 2004, 5:183doi:10.1186/1471-2105-5-183


Published:	25 November 2004

Abstract

Background

This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.

Results

Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used.

Conclusions

Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.