Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.

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With the avalanche of the newly found protein sequences in the post-genomic epoch, there is an increasing trend for annotating a number of newly discovered enzyme sequences. Among the various proteins, enzyme was considered as the one of the largest kind of proteins. It takes part in most of the biochemical reactions and plays a key role in metabolic pathways. Multifunctional enzyme is enzyme that plays multiple physiological roles. Given a multifunctional enzyme sequence, how can we identify its class? Especially, how can we deal with the multi-classes problem since an enzyme may simultaneously belong to two or more functional classes? To address these problems, which are obviously very important both to basic research and drug development, a multi-label classifier was developed via three different prediction models with multi-label K-nearest algorithm. Experimental results obtained on a stringent benchmark dataset of enzymes by jackknife cross-validation test show that the predicting results were exciting, indicating that the current method could be an effective and promising high throughput method in the enzyme research. We hope it could play an important complementary role to the existing predictors in identifying the classes of enzymes.