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.
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
Related Journals
Current Protein & Peptide Science
Related Books
Frontiers in Protein and Peptide Sciences
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