title: Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information.
abstract: Self-interacting proteins (SIPs) play an essential role in cellular functions and the evolution of protein interaction networks (PINs). Due to the limitations of experimental self-interaction proteins detection technology, it is a very important task to develop a robust and accurate computational approach for SIPs prediction. In this study, we propose a novel computational method for predicting SIPs from protein amino acids sequence. Firstly, a novel feature representation scheme based on Local Binary Pattern (LBP) is developed, in which the evolutionary information, in the form of multiple sequence alignments, is taken into account. Then, by employing the Relevance Vector Machine (RVM) classifier, the performance of our proposed method is evaluated on yeast and human datasets using a five-fold cross-validation test. The experimental results show that the proposed method can achieve high accuracies of 94.82% and 97.28% on yeast and human datasets, respectively. For further assessing the performance of our method, we compared it with the state-of-the-art Support Vector Machine (SVM) classifier, and other existing methods, on the same datasets. Comparison results demonstrate that the proposed method is very promising and could provide a cost-effective alternative for predicting SIPs. In addition, to facilitate extensive studies for future proteomics research, a web server is freely available for academic use at .
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
authors.forename: Ji-Yong
authors.initials: JY
authors.lastname: An
authors.affiliation: Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Ürümqi 830011, China. zhuhongyou@gmail.com.
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, Jiangsu 221116, China. xingchen@amss.ac.cn.
authors.forename: Xing
authors.initials: X
authors.lastname: Chen
authors.affiliation: School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.affiliation: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
authors.forename: Guiying
authors.initials: G
authors.lastname: Yan
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China.
authors.forename: Da-Fu
authors.initials: DF
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D019143:Evolution, Molecular; D006801:Humans; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D064878:Web Browser
chemical_list: D000596:Amino Acids; D011506:Proteins