title: Predicting protein interaction sites from residue spatial sequence profile and evolution rate.
abstract: This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residue's evolutionary conservation score based on a phylogenetic tree. Three predictors using a support vector machines algorithm are constructed to predict whether a surface residue is a part of a protein-protein interface. The efficiency and the effectiveness of our proposed approach is verified by its better prediction performance compared with other models. The study is based on a non-redundant data set of heterodimers consisting of 69 protein chains.
authors.affiliation: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Wang
authors.forename: Peng
authors.initials: P
authors.lastname: Chen
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
authors.forename: Jing-jing
authors.initials: JJ
authors.lastname: Li
authors.forename: Tat-Ming
authors.initials: TM
authors.lastname: Lok
authors.forename: Michael R
authors.initials: MR
authors.lastname: Lyu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019143:Evolution, Molecular; D046911:Macromolecular Substances; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D020539:Sequence Analysis, Protein
chemical_list: D046911:Macromolecular Substances; D011506:Proteins