Identify catalytic triads of serine hydrolases by support vector machines.
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.
Cai
Yu-dong
YD
Shanghai Research Center of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
Zhou
Guo-Ping
GP
Jen
Chin-Hung
CH
Lin
Shuo-Liang
SL
Chou
Kuo-Chen
KC
D000465:Algorithms; D002384:Catalysis; D020134:Catalytic Domain; D019295:Computational Biology / Q000379:methods; D008956:Models, Chemical; D015233:Models, Statistical; D011487:Protein Conformation; D012697:Serine Endopeptidases / Q000737:chemistry*; D013329:Structure-Activity Relationship
D012697:Serine Endopeptidases