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Bioinformatics Advance Access published online on May 12, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp309
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© The Author (2009). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

ESG: Extended Similarity Group Method for Automated Protein Function Prediction

Chitale Meghana 1, Hawkins Troy 2, Park Changsoon 2,* and Kihara Daisuke 2,1,4,*

1Department of Computer Science, Purdue University, Indiana 47907, USA.
2Department of Biological Sciences, Purdue University, Indiana 47907, USA.
3Department of Statistics, Chung-Ang University, Seoul 156-756, Korea (South).
4Markey Center for Structural Biology, Purdue University, Indiana 47907, USA.

*To whom correspondence should be addressed. Prof. Daisuke Kihara, E-mail: dkihara{at}purdue.edu, dkiharadk{at}yahoo.co.jp


  Abstract

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 as-signed 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 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: dkihara{at}purdue.edu, cspark{at}cau.ac.kr

Associate Editor: Dr. Limsoon Wong

Received on January 8, 2009; revised on April 16, 2009; accepted on May 5, 2009

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