Using homology relations within a database markedly boosts protein sequence similarity search

Jing Tong, Ruslan I. Sadreyev, Jimin Pei, Lisa N. Kinch, Nick V. Grishin

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Inference of homology from protein sequences provides an essential tool for analyzing protein structure, function, and evolution. Current sequence-based homology search methods are still unable to detect many similarities evident from protein spatial structures. In computer science a search engine can be improved by considering networks of known relationships within the search database. Here, we apply this idea to protein-sequence-based homology search and show that it dramatically enhances the search accuracy. Our new method, COMPADRE (COmparison of Multiple Protein sequence Alignments using Database RElationships) assesses the relationship between the query sequence and a hit in the database by considering the similarity between the query and hit's known homologs. This approach increases detection quality, boosting the precision rate from 18% to 83% at half-coverage of all database homologs. The increased precision rate allows detection of a large fraction of protein structural relationships, thus providing structure and function predictions for previously uncharacterized proteins. Our results suggest that this general approach is applicable to a wide variety of methods for detection of biological similarities. The web server is available at prodata.swmed.edu/compadre.

Original languageEnglish (US)
Pages (from-to)7003-7008
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number22
DOIs
Publication statusPublished - Jun 2 2015

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Keywords

  • Homology detection
  • Homology network
  • Protein modeling
  • Remote sequence similarity search
  • Similarity score

ASJC Scopus subject areas

  • General

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