Prediction of interaction between small molecule and enzyme using AdaBoost.
The knowledge of whether one enzyme can interact with a small molecule is essential for understanding the molecular and cellular functions of organisms. In this paper, we introduce a classifier to predict the small molecule- enzyme interaction, i.e., whether they can interact with each other. Small molecules are represented by their chemical functional groups, and enzymes are represented by their biochemical and physicochemical properties, resulting in a total of 160 features. These features are input into the AdaBoost classifier, which is known to have good generalization ability to predict interaction. As a result, the overall prediction accuracy, tested by tenfold cross-validation and independent sets, is 81.76% and 83.35%, respectively, suggesting that this strategy is effective. In this research, we typically choose interactions between small molecules and enzymes involved in metabolism to ultimately improve further understanding of metabolic pathways. An online predictor developed by this research is available at http://chemdata.shu.edu.cn/small_m .
Niu
Bing
B
Shanghai University, Shanghai 200072, China.
Jin
Yuhuan
Y
Lu
Lin
L
Fen
Kaiyan
K
Gu
Lei
L
He
Zhisong
Z
Lu
Wencong
W
Li
Yixue
Y
Cai
Yudong
Y
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D055598:Chemical Phenomena; D016208:Databases, Factual; D004798:Enzymes / Q000737:chemistry* / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods; D015203:Reproducibility of Results
D004798:Enzymes