Bioinformatics Advance Access originally published online on May 12, 2009
Bioinformatics 2009 25(14):1739-1745; doi:10.1093/bioinformatics/btp309
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ESG: extended similarity group method for automated protein function prediction
1 Department of Computer Science, 2 Department of Biological Sciences, Purdue University, IN 47907, USA, 3 Department of Statistics, Chung-Ang University, Seoul 156-756, Korea and 4 Markey Center for Structural Biology, Purdue University, IN 47907, USA
* To whom correspondence should be addressed.
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Abstract |
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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{at}cau.ac.kr; dkihara{at}purdue.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Limsoon Wong
Received on January 8, 2009; revised on April 16, 2009; accepted on May 5, 2009
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