Investigating the binding area of protein surface using MCL algorithm

Proteins combine with other materials to achieve a variety of functions, which will be similar if their active sites are similar. Thus we can infer a proteinpsilas function by identifying its binding area. This paper proposes a novel method to select a proteinpsilas binding area using the Markov Cluster (MCL) algorithm. A distance matrix is constructed from the surface residues distance on the protein, then transformed to the connectivity matrix for application of the MCL process, and finally evaluated by using Catalytic Site Atlas (CSA) data. In the experimental result using CSA data which comprised 94 selected single chain proteins, our algorithm detects 91 (97%) binding areas near the active site of each protein. We introduced new geometrical features with the aim of improving the prediction accuracy of the active site residues by selecting the residues near the active site.