Enzyme classification using reactive motifs

Reactive motifs are short conserved sub–sequences discovered from functional sites of enzyme sequences, and can be used as an effective representation of enzyme sequences. However, the lack of site information leads to low–coverage reactive motifs. With the use of background knowledge, a motif generalisation method is required to increase reactive motifs' coverage. We show that a fuzzy concept lattice (FCL) provides an efficient representation of both single–value and multi–value biological background knowledge and an efficient computational support for generalising reactive motifs. Compared to statistical and expert–based motifs, we show that the generalised reactive motifs using FCL with SVM classifier produce satisfactory accuracy in classifying new enzymes. Further, they improve interpretability of the classification results and provide more biological evidences to biologists. All of the generalised reactive motifs are relevant to the functional sites, and the way they are combined to perform protein function is useful for numerous applications in bioinformatics.