Predicting protein–protein interactions based only on sequences information
- Juwen Shen†,
- Jian Zhang†,
- Xiaomin Luo†,
- Weiliang Zhu†,‡,
- Kunqian Yu†,
- Kaixian Chen†,
- Yixue Li§, and
- Hualiang Jiang†,‡,¶
- †Center for Drug Discovery and Design, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and Graduate School of Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China;
- ‡School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; and
- §Bioinformation Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
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Edited by Michael Levitt, Stanford University School of Medicine, Stanford, CA, and approved December 28, 2006 (received for review September 8, 2006)
Abstract
Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.
Footnotes
- ¶To whom correspondence should be addressed. E-mail: hljiang{at}mail.shcnc.ac.cn
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Author contributions: J.S. and J.Z. contributed equally to this work; H.J. designed research; J.S., J.Z., and X.L. performed research; J.S., J.Z., X.L., W.Z., K.Y., K.C., Y.L., and H.J. analyzed data; and J.S., J.Z., W.Z., K.C., Y.L., and H.J. wrote the paper.
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The authors declare no conflict of interest.
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This article is a PNAS direct submission.
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This article contains supporting information online at www.pnas.org/cgi/content/full/0607879104/DC1.
- Abbreviations:
- PPI,
- protein–protein interaction;
- SVM,
- support vector machine.
- © 2007 by The National Academy of Sciences of the USA
