Predicting enzyme family class in a hybridization space

• Published in: Protein science Vol. 13; no. 11; pp. 2857 - 2863
• Main Authors: Chou, Kuo‐Chen, Cai, Yu‐Dong
• Format: Journal Article
• Language: English
• Published: Bristol Cold Spring Harbor Laboratory Press 01.11.2004
• Subjects: 40% cutoff; Amino Acid Sequence; bioinformatics; ENZYME database; Enzymes - chemistry; Enzymes - classification; Gene Ontology; GO‐PseAA predictor; ISort predictor; Models, Molecular; Models, Theoretical; proteomics; Proteomics - methods; pseudo‐amino‐acid composition; quasi‐sequence‐order effect
• ISSN: 0961-8368; 1469-896X
• DOI: 10.1110/ps.04981104

Given the sequence of a protein, how can we predict whether it is an enzyme or a non‐enzyme? If it is, what enzyme family class it belongs to? Because these questions are closely relevant to the biological function of a protein and its acting object, their importance is self‐evident. Particularly with the explosion of protein sequences entering into data banks and the relatively much slower progress in using biochemical experiments to determine their functions, it is highly desired to develop an automated method that can be used to give fast answers to these questions. By hybridizing the gene ontology and pseudo‐amino‐acid composition, we have introduced a new method that is called GO‐PseAA predictor and operate it in a hybridization space. To avoid redundancy and bias, demonstrations were performed on a data set in which none of the proteins in an individual class has ≥40% sequence identity to any other. The overall success rate thus obtained by the jackknife cross‐validation test in identifying enzyme and non‐enzyme was 93%, and that in identifying the enzyme family was 94% for the following six main Enzyme Commission (EC) classes: (1) oxidoreductase, (2) transferase, (3) hydrolase, (4) lyase, (5) isomerase, and (6) ligase. The corresponding rates by the independent data set test were 98% and 97%, respectively.