Identify catalytic triads of serine hydrolases by support vector machines |
| |
Authors: | Cai Yu-dong Zhou Guo-Ping Jen Chin-Hung Lin Shuo-Liang Chou Kuo-Chen |
| |
Institution: | Shanghai Research Center of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk |
| |
Abstract: | The core of an enzyme molecule is its active site from the viewpoints of both academic research and industrial application. To reveal the structural and functional mechanism of an enzyme, one needs to know its active site; to conduct structure-based drug design by regulating the function of an enzyme, one needs to know the active site and its microenvironment as well. Given the atomic coordinates of an enzyme molecule, how can we predict its active site? To tackle such a problem, a distance group approach was proposed and the support vector machine algorithm applied to predict the catalytic triad of serine hydrolase family. The success rate by jackknife test for the 139 serine hydrolases was 85%, implying that the method is quite promising and may become a useful tool in structural bioinformatics. |
| |
Keywords: | Distance-group Support vector machine Catalytic triad Serine hydrolase Structural bioinformatics |
本文献已被 ScienceDirect PubMed 等数据库收录! |