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

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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.





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

Document Type: Research Article

Publication date: 2012-01-01

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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