title: Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.
abstract: Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research.
authors: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China. zwli@cumt.edu.cn.
authors: Zheng-Wei
authors: ZW
authors: Li
authors: Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China. zhuhongyou@ms.xjb.ac.cn.
authors: Zhu-Hong
authors: ZH
authors: You
authors: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 21116, China. xingchen@amss.ac.cn.
authors: Xing
authors: X
authors: Chen
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China. guijie@ustc.edu.
authors: Jie
authors: J
authors: Gui
authors: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 21116, China. nr@cumt.edu.cn.
authors: Ru
authors: R
authors: Nie
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D025941:Protein Interaction Mapping / Q000379:methods*; D040901:Proteomics / Q000379:methods; D060388:Support Vector Machine
keywords: discriminative vector machine; evolutionary information; physicochemical characteristics; protein interactions; protein sequence