Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites

The purpose of this research is to search for motifs directly at binding and catalytic sites called reactive motifs, and then to predict enzyme functions from the discovered reactive motifs. The main challenge is that the data of binding, or catalytic sites is only available in the range 3.34% of all enzymes, and many of each data provides only one sequence record. The other challenge is the complexity of motif combinations to predict enzyme functions. In this paper, we introduce a unique process which combines statistics with bio-chemistry background to determine reactive motifs. It is consisting of block scan filter, mutation control, and reactive site-group define procedures. The purpose of block scan filter is to alter each 1-sequence record of binding or catalytic site, using similarity score, to produce quality blocks. These blocks are input to mutation control, where in each position of the sequences, amino acids are analyzed an extended to determine complete substitution group. Output of the mutation control step is a set of motifs for each 1-sequence record input. These motifs are then grouped using the reactive site-group define procedure to produce reactive motifs. Those reactive motifs together with known enzyme sequence dataset are used as the input to C4.5 learning algorithm, to obtain an enzyme prediction model. The accuracy of this model is checked against testing dataset. At 235 enzyme function class, the reactive motifs yield the best prediction result with C4.5 at 72.58%, better than PROSITE motifs.