Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition.
A new method has been developed to predict the enzymatic attribute of proteins by hybridizing the gene product composition and pseudo amino acid composition. As a demonstration, a working dataset was generated with a cutoff of 60% sequence identity to avoid redundancy and bias in statistical prediction. The dataset thus constructed contains 39989 protein sequences, of which 27469 are non-enzymes and 12520 enzymes that were further classified into 6 enzyme family classes according to their 6 main EC (Enzyme Commission) numbers (2314 are oxidoreductases, 3653 transferases, 3246 hydrolases, 1307 lyases, 676 isomerases, and 1324 ligases). The overall success rate by the jackknife test for the identification between enzyme and non-enzyme was 94%, and that for the identification among the 6 enzyme family classes was 98%. It is anticipated that, with the rapid increase of protein sequences entering into databanks, the current method will become a useful automated tool in identifying the enzymatic attribute of a newly found protein sequence.
Cai
Yu-Dong
YD
Biomolecular Sciences Department, University of Manchester Institute of Science & Technology, PO Box 88, Manchester, M60 1QD, UK
Zhou
Guo-Ping
GP
Chou
Kuo-Chen
KC
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis*; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000145:classification* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods
D000596:Amino Acids; D004798:Enzymes