Prediction of active site cleft using support vector machines.

Computational tools are available today for the detection and delineation of the clefts and cavities in protein 3D structure and ranking them on the basis of probable binding site clefts. There is a need to improve the ranking of clefts and accuracy of predicting catalytic site clefts. Our results show that the distance of the clefts from protein centroid and sequence entropy of the lining residues, when used in conjunction with the volume, are valuable descriptors for predicting the catalytic site. We have applied the SVM approach for recognizing and ranking the active site clefts and tested its performance using different combinations of attributes. In both the ligand-bound and the unbound forms of structures, our method correctly predicts the active site clefts in 73% of cases at rank one. If we consider the results at rank 3 (i.e., the correct solution is among one of the top three solutions), the correctly predicted cases are 94% and 90% for the bound and the unbound forms of structures, respectively. Our approach improves the ranking of binding site clefts in comparison with CASTp and is comparable to other existing methods like Fpocket. Although the data set for training the SVM approach is rather small in size, the results are encouraging for the method to be used as complementary to other existing tools.