A simple extension to the CMASA method for the prediction of catalytic residues in the presence of single point mutations

The automatic identification of catalytic residues still remains an important challenge in<br>structural bioinformatics. Sequence-based methods are good alternatives when the<br>query shares a high percentage of identity with a well-annotated enzyme. However,<br>when the homology is not apparent, which occurs with many structures from the<br>structural genome initiative, structural information should be exploited. A local<br>structural comparison is preferred to a global structural comparison when predicting<br>functional residues. CMASA is a recently proposed method for predicting catalytic<br>residues based on a local structure comparison. The method achieves high accuracy<br>and a high value for the Matthews correlation coefficient. However, point substitutions<br>or a lack of relevant data strongly affect the performance of the method. In the present<br>study, we propose a simple extension to the CMASA method to overcome this<br>difficulty. Extensive computational experiments are shown as proof of concept<br>instances, as well as for a few real cases. The results show that the extension<br>performs well when the catalytic site contains mutated residues or when some<br>residues are missing. The proposed modification could correctly predict the catalytic<br>residues of a mutant transferase of the kinase family, 1EVF. It also successfully<br>predicted the catalytic residues for 3HRC despite the lack of information for a relevant<br>side chain atom in the PDB file.