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Research Article
A discriminative approach for identifying domain–domain interactions from protein–protein interactions
Article first published online: 26 OCT 2009
DOI: 10.1002/prot.22643
Copyright © 2009 Wiley-Liss, Inc.
Issue
Proteins: Structure, Function, and Bioinformatics
Volume 78, Issue 5, pages 1243–1253, April 2010
Additional Information
How to Cite
Zhao, X.-M., Chen, L. and Aihara, K. (2010), A discriminative approach for identifying domain–domain interactions from protein–protein interactions. Proteins: Structure, Function, and Bioinformatics, 78: 1243–1253. doi: 10.1002/prot.22643
Publication History
- Issue published online: 8 FEB 2010
- Article first published online: 26 OCT 2009
- Accepted manuscript online: 26 OCT 2009 12:00AM EST
- Manuscript Accepted: 14 OCT 2009
- Manuscript Revised: 4 OCT 2009
- Manuscript Received: 30 JUN 2009
Funded by
- National High Technology Research and Development Program of China. Grant Number: 2006AA02Z309
- Innovation Program of Shanghai Municipal Education Commission. Grant Numbers: 09ZZ93, 10YZ01
- Chief Scientist Program of Shanghai Institutes for Biological Sciences
- Chinese Academy of Sciences. Grant Number: 2009CSP002
- Innovation Funding of Shanghai University
- Open Funding of National Key Laboratory of Plant Molecular Genetics
- SRF for ROCS
- SEM
- JSPS-NSFC
- Abstract
- Article
- References
- Cited By
Keywords:
- protein–protein intraction;
- domain–domain intraction;
- feature selection;
- discriminative approach;
- classification
Abstract
Protein domains are functional and structural units of proteins. Therefore, identification of domain–domain interactions (DDIs) can provide insight into the biological functions of proteins. In this article, we propose a novel discriminative approach for predicting DDIs based on both protein–protein interactions (PPIs) and the derived information of non-PPIs. We make a threefold contribution to the work in this area. First, we take into account non-PPIs explicitly and treat the domain combinations that can discriminate PPIs from non-PPIs as putative DDIs. Second, DDI identification is formalized as a feature selection problem, in which it tries to find out a minimum set of informative features (i.e., putative DDIs) that discriminate PPIs from non-PPIs, which is plausible in biology and is able to predict DDIs in a systematic and accurate manner. Third, multidomain combinations including two-domain combinations are taken into account in the proposed method, where multidomain cooperations may help proteins to interact with each other. Numerical results on several DDI prediction benchmark data sets show that the proposed discriminative method performs comparably well with other top algorithms with respect to overall performance, and outperforms other methods in terms of precision. The PPI data sets used for prediction of DDIs and prediction results can be found at http://csb.shu.edu.cn/dipd. Proteins 2010. © 2009 Wiley-Liss, Inc.