A Nearest Neighbor Algorithm Based Predictor for the Prediction of Enzyme - Small Molecule Interaction

Title: A Nearest Neighbor Algorithm Based Predictor for the Prediction of Enzyme - Small Molecule Interaction

Volume: 19 Issue: 1

Author(s): Le-Le Hu, Zhi-Song He, Xiao-He Shi, Xiang-Ying Kong, Hai-Peng Li and Wen-Cong Lu

Affiliation:

Keywords: Biochemical and physicochemical description of proteins, Chemical functional group, Feature selection, Forward Feature Selection, Incremental Feature Selection, Minimum Redundancy Maximum Relevance, jackknife cross-validation, NNA, KEGG, mRMR, FFS, hydrophobicity, FRET, RTK signaling, NMR

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.

Cite this article as:

Hu Le-Le, He Zhi-Song, Shi Xiao-He, Kong Xiang-Ying, Li Hai-Peng and Lu Wen-Cong, A Nearest Neighbor Algorithm Based Predictor for the Prediction of Enzyme - Small Molecule Interaction, Protein & Peptide Letters 2012; 19 (1) . https://dx.doi.org/10.2174/092986612798472938