title: Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.
abstract: Identification of protein binding sites is critical for studying the function of the proteins. In this paper, we proposed a method for protein binding site prediction, which combined the order profile propensities and hidden Markov support vector machine (HM-SVM). This method employed the sequential labeling technique to the field of protein binding site prediction. The input features of HM-SVM include the profile-based propensities, the Position-Specific Score Matrix (PSSM), and Accessible Surface Area (ASA). When tested on different data sets, the proposed method showed promising results, and outperformed some closely relative methods by more than 10% in terms of AUC.
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
authors.forename: Bingquan
authors.initials: B
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Fule
authors.initials: F
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China ; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China.
authors.forename: Xiaolong
authors.initials: X
authors.lastname: Wang
mesh_terms: D001665:Binding Sites; D008956:Models, Chemical; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D011506:Proteins