title: An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences.
abstract: Protein-Protein Interactions (PPI) is not only the critical component of various biological processes in cells, but also the key to understand the mechanisms leading to healthy and diseased states in organisms. However, it is time-consuming and cost-intensive to identify the interactions among proteins using biological experiments. Hence, how to develop a more efficient computational method rapidly became an attractive topic in the post-genomic era. In this paper, we propose a novel method for inference of protein-protein interactions from protein amino acids sequences only. Specifically, protein amino acids sequence is firstly transformed into Position-Specific Scoring Matrix (PSSM) generated by multiple sequences alignments; then the Pseudo PSSM is used to extract feature descriptors. Finally, ensemble Rotation Forest (RF) learning system is trained to predict and recognize PPIs based solely on protein sequence feature. When performed the proposed method on the three benchmark data sets (Yeast, H. pylori, and independent dataset) for predicting PPIs, our method can achieve good average accuracies of 98.38%, 89.75%, and 96.25%, respectively. In order to further evaluate the prediction performance, we also compare the proposed method with other methods using same benchmark data sets. The experiment results demonstrate that the proposed method consistently outperforms other state-of-the-art method. Therefore, our method is effective and robust and can be taken as a useful tool in exploring and discovering new relationships between proteins. A web server is made publicly available at the URL http://202.119.201.126:8888/PsePSSM/ for academic use.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.|College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Jian-Qiang
authors.initials: JQ
authors.lastname: Li
authors.affiliation: School of Foreign Languages, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Yan
authors.affiliation: College of Information Science and Engineering, Zaozhuang University, Zaozhuang, Shandong 277100, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Yu-An
authors.initials: YA
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000818:Animals; D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016480:Helicobacter pylori / Q000235:genetics / Q000378:metabolism; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D016415:Sequence Alignment; D020539:Sequence Analysis, Protein
chemical_list: D001426:Bacterial Proteins; D029701:Saccharomyces cerevisiae Proteins
keywords: cancer; disease; multiple sequences alignments; position-specific scoring matrix