title: Using functional domain composition to predict enzyme family classes.
abstract: According to their main EC (Enzyme Commission) numbers, enzymes are classified into the following 6 main classes: oxidoreductases, transferases, hydrolases, lyases, isomerases, and ligases. A new method has been developed to predict the enzymatic attribute of proteins by introducing the functional domain composition to formulate a given protein sequence. The advantage by doing so is that both the sequence-order-related features and the function-related features are naturally incorporated in the predictor. As a demonstration, the jackknife cross-validation test was performed on a dataset that consists of proteins with only less than 20% sequence identity to each other in order to get rid of any homologous bias. The overall success rate thus obtained was 85% in identifying the enzyme family classes (including the identification of nonenzyme protein sequences as well). The success rate is significantly higher than those obtained by the other methods on such a stringent dataset. This indicates that using the functional domain composition to represent protein samples for statistical prediction is indeed very promising, and will become a powerful tool in bioinformatics and proteomics.
authors.affiliation: Biomolecular Sciences Department, University of Manchester Institute of Science & Technology, Manchester, M60 1QD, United Kingdom. y.cai@umist.ac.uk
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000145:classification*; D008722:Methods; D017434:Protein Structure, Tertiary; D040681:Structural Homology, Protein
chemical_list: D004798:Enzymes