title: Inferring protein-protein interacting sites using residue conservation and evolutionary information.
abstract: This paper proposes a novel method using protein residue conservation and evolution information, i.e., spatial sequence profile, sequence information entropy and evolution rate, to infer protein binding sites. Some predictors based on support vector machines (SVMs) algorithm are constructed to predict the role of surface residues in protein-protein interface. By combining protein residue characters, the prediction performance can be improved obviously. We then made use of the predicted labels of neighbor residues to improve the performance of the predictors. The efficiency and the effectiveness of our proposed approach are verified by its better prediction performance based on a non-redundant data set of heterodimers.
authors.affiliation: Hefei Institute Intelligent Machines, Chinese Academy of Science, Hefei, Anhui, 230031, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
authors.forename: Hau San
authors.initials: HS
authors.lastname: Wong
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000595:Amino Acid Sequence; D017124:Conserved Sequence; D019277:Entropy; D019143:Evolution, Molecular; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D012680:Sensitivity and Specificity; D013499:Surface Properties
chemical_list: D011506:Proteins