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Bioinformatics Advance Access published online on January 18, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl660
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Protein-protein interaction site prediction based on conditional random fields

Ming-Hui Li *, Xiao-Long Wang , Lei Lin and Tao Liu

Bioinformatics Research Group, ITNLP Lab, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

*To whom correspondence should be addressed. Ming-Hui Li, Email: mhli{at}insun.hit.edu.cn


  Abstract

Motivation: We are motivated by the fast-growing number of protein structures in the Protein Data Bank with necessary information for prediction of protein-protein interaction sites to develop methods for identification of residues participating in protein-protein interactions. We would like to compare conditional random fields (CRFs)-based method with conventional classification-based methods which omit the relation between two labels of neighboring residues to show the advantages of CRFs-based method in predicting protein-protein interaction sites.

Results: The prediction of protein-protein interaction sites is solved as a sequential labeling problem by applying CRFs with features including protein sequence profile and residue accessible surface area. The CRFs-based method can achieve a comparable performance with state-of-the-art methods, when 1276 nonredundant hetero-complex protein chains are used as training and test set. Experimental result shows that CRFs-based method is a powerful and robust protein-protein interaction site prediction method and can be used to guide biologists to make specific experiments on proteins.

Availability: http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html

Associate Editor: Trey Ideker

Received on September 11, 2006; revised on December 3, 2006; accepted on December 20, 2006

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