title: A nearest neighbor algorithm based predictor for the prediction of enzyme-small molecule interaction.
abstract: It is of great use to find out and clear up the interactions between enzymes and small molecules, for understanding the molecular and cellular functions of organisms. In this study, we developed a novel method for the prediction of enzyme-small molecules interactions based on machine learning approach. The biochemical and physicochemical description of proteins and the functional group composition of small molecules are used for representing enzyme-small molecules pairs. Tested by jackknife cross-validation, our predictor achieved an overall accuracy of 87.47%, showing an acceptable efficiency. The 39 features selected by feature selection were analyzed for further understanding of enzyme-small molecule interactions.
authors.affiliation: Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China.
authors.forename: Le-Le
authors.initials: LL
authors.lastname: Hu
authors.forename: Zhi-Song
authors.initials: ZS
authors.lastname: He
authors.forename: Xiao-He
authors.initials: XH
authors.lastname: Shi
authors.forename: Xiang-Ying
authors.initials: XY
authors.lastname: Kong
authors.forename: Hai-Peng
authors.initials: HP
authors.lastname: Li
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology; D030562:Databases, Protein; D057927:Hydrophobic and Hydrophilic Interactions; D008969:Molecular Sequence Data; D011237:Predictive Value of Tests; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D054852:Small Molecule Libraries