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PubMed · 15707365

Using functional domain composition to predict enzyme family classes.

Yu-Dong Cai·Kuo-Chen Chou
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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.

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AlgorithmsDatabases, ProteinEnzymesMethodsProtein Structure, TertiaryStructural Homology, ProteinJournal Article

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Yu-Dong Cai, Kuo-Chen Chou. Using functional domain composition to predict enzyme family classes.. https://doi.org/10.1021/pr049835p

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