Bioinformatics 20(Suppl. 1) © Oxford University Press 2004; all rights reserved.
A two-stage classifier for identification of protein–protein interface residues
1 Artificial Intelligence Research Laboratory, 2 Department of Computer Science, 3 Department of Genetics, Development and Cell Biology, 4 Laurence H Baker Center for Bioinformatics and Biological Statistics, 5 Bioinformatics and Computational Biology Graduate Program and 6 Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, IA, 50010, USA
Received on January 15, 2004; accepted on March 1, 2004
Motivation: The ability to identify protein–protein interaction sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks.
Results: We have developed a two-stage method consisting of a support vector machine (SVM) and a Bayesian classifier for predicting surface residues of a protein that participate in protein–protein interactions. This approach exploits the fact that interface residues tend to form clusters in the primary amino acid sequence. Our results show that the proposed two-stage classifier outperforms previously published sequence-based methods for predicting interface residues. We also present results obtained using the two-stage classifier on an independent test set of seven CAPRI (Critical Assessment of PRedicted Interactions) targets. The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes.
Supplementary information: http://www.public.iastate.edu/~chhyan/ISMB2004/list.html
Contact: chhyan{at}iastate.edu
* To whom correspondence should be addressed.
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