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PubMed · 15563734

Protein family comparison using statistical models and predicted structural information.

Richard Chung·Golan Yona
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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.

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Amino Acid SequenceModels, StatisticalPredictive Value of TestsProtein Structure, SecondaryProteinsSequence Homology, Amino AcidComparative StudyJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.

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Richard Chung, Golan Yona. 2004-11-25. Protein family comparison using statistical models and predicted structural information.. https://doi.org/10.1186/1471-2105-5-183

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