title: Identify catalytic triads of serine hydrolases by support vector machines.
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.
authors.affiliation: Shanghai Research Center of Biotechnology, Chinese Academy of Sciences, Shanghai 200233, China. y.cai@umist.ac.uk
authors.forename: Yu-dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Guo-Ping
authors.initials: GP
authors.lastname: Zhou
authors.forename: Chin-Hung
authors.initials: CH
authors.lastname: Jen
authors.forename: Shuo-Liang
authors.initials: SL
authors.lastname: Lin
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: 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
chemical_list: D012697:Serine Endopeptidases