Journal Article

ESG: extended similarity group method for automated protein function prediction

Meghana Chitale, Troy Hawkins, Changsoon Park and Daisuke Kihara

in Bioinformatics

Volume 25, issue 14, pages 1739-1745
Published in print July 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp309
ESG: extended similarity group method for automated protein function prediction

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Motivation: Importance of accurate automatic protein function prediction is ever increasing in the face of a large number of newly sequenced genomes and proteomics data that are awaiting biological interpretation. Conventional methods have focused on high sequence similarity-based annotation transfer which relies on the concept of homology. However, many cases have been reported that simple transfer of function from top hits of a homology search causes erroneous annotation. New methods are required to handle the sequence similarity in a more robust way to combine together signals from strongly and weakly similar proteins for effectively predicting function for unknown proteins with high reliability.

Results: We present the extended similarity group (ESG) method, which performs iterative sequence database searches and annotates a query sequence with Gene Ontology terms. Each annotation is assigned with probability based on its relative similarity score with the multiple-level neighbors in the protein similarity graph. We will depict how the statistical framework of ESG improves the prediction accuracy by iteratively taking into account the neighborhood of query protein in the sequence similarity space. ESG outperforms conventional PSI-BLAST and the protein function prediction (PFP) algorithm. It is found that the iterative search is effective in capturing multiple-domains in a query protein, enabling accurately predicting several functions which originate from different domains.

Availability: ESG web server is available for automated protein function prediction at http://dragon.bio.purdue.edu/ESG/

Contact: cspark@cau.ac.kr; dkihara@purdue.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5002 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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