Prediction of interaction between small molecule and enzyme using AdaBoost |
| |
| Authors: | Bing Niu Yuhuan Jin Lin Lu Kaiyan Fen Lei Gu Zhisong He Wencong Lu Yixue Li Yudong Cai |
| |
| Affiliation: | (1) Shanghai University, 149 Yan-Chang Road, Shanghai, 200072, China;(2) Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai, 200444, China;(3) Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200040, China;(4) Division of Imaging Science &; Biomedical Engineering, The University of Manchester, Room G424, Stopford Building, Manchester, M13 9PT, UK;(5) Bioinformatics Center, Key Lab of Molecular Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China;(6) Department of Bioinformatics, College of Life Sciences, Zhejiang University, HangZhou, ZheJiang, 310058, China;(7) Shanghai Center for Bioinformation Technology, Shanghai, 200235, China;(8) College of Life Science&; Biotechnology, Shanghai Jiao Tong University, Shanghai, China;(9) Institute of System Biology, Shanghai University, 99 ShangDa Road, Shanghai, 200244, China;(10) Department of Combinatorics and Geometry, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China; |
| |
| Abstract: | 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 . Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. |
| |
| Keywords: | Small molecule– enzyme couple Chemical functional group Biochemical and physicochemical properties AdaBoost Metabolic pathway |
| 本文献已被 SpringerLink 等数据库收录！ |
|
