文献类型：期刊文献

英文题名：Predictina protein-protein interactions based only on sequences information

作者：Shen, Juwen; Zhang, Jian; Luo, Xiaomin; Zhu, Weiliang; Yu, Kunqian; Chen, Kaixian; Li, Yixue; Jiang, Hualiang

机构：[1]Chinese Acad Sci, Ctr Drug Discovery & Design, State Key Lab Drug Res, Shanghai Inst Mat Med,Shanghai Inst Biol Sci, Shanghai 201203, Peoples R China;[2]Chinese Acad Sci, Grad Sch, Shanghai 201203, Peoples R China;[3]E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China;[4]Chinese Acad Sci, Shanghai Inst Biol Sci, Bioinformat Ctr, Shanghai 200031, Peoples R China

年份：2007

卷号：104

期号：11

起止页码：4337

外文期刊名：PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000244972700019)】；

语种：英文

外文关键词：conjoint triad; support vector machine

摘要：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.