title: PECB: prediction of enzyme catalytic residues based on Naive Bayes classification.
abstract: In the post-genome era, huge numbers of protein structures accumulate, but little is known about their function. It is time consuming and labour intensive to investigate them, e.g., enzyme catalytic properties, through in vivo or in vitro work. So in silico predictions could be a promising strategy to greatly shrink the list of potential targets. This work incorporated both structural and physico-chemical information into a Naive Bayes classification system, and gained much better performance. The ten-fold cross validation results of this method could reach 88.6% of sensitivity and 93.7% of specificity. The improvement of prediction accuracy is detailed in this paper. The PECB is also applied to predict other important sites.
authors.affiliation: Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, 230027, China. kpzhang@mail.ustc.edu.cn
authors.forename: Kunpeng
authors.initials: K
authors.lastname: Zhang
authors.forename: Yun
authors.initials: Y
authors.lastname: Xu
authors.forename: Guoliang
authors.initials: G
authors.lastname: Chen
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D001499:Bayes Theorem; D001665:Binding Sites; D002384:Catalysis; D003198:Computer Simulation; D004789:Enzyme Activation; D004798:Enzymes / Q000737:chemistry*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D000596:Amino Acids; D004798:Enzymes