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 instructural bioinformatics. Sequence-based methods are good alternatives when thequery shares a high percentage of identity with a well-annotated enzyme. However,when the homology is not apparent, which occurs with many structures from thestructural genome initiative, structural information should be exploited. A localstructural comparison is preferred to a global structural comparison when predictingfunctional residues. CMASA is a recently proposed method for predicting catalyticresidues based on a local structure comparison. The method achieves high accuracyand a high value for the Matthews correlation coefficient. However, point substitutionsor a lack of relevant data strongly affect the performance of the method. In the presentstudy, we propose a simple extension to the CMASA method to overcome thisdifficulty. Extensive computational experiments are shown as proof of conceptinstances, as well as for a few real cases. The results show that the extensionperforms well when the catalytic site contains mutated residues or when someresidues are missing. The proposed modification could correctly predict the catalyticresidues of a mutant transferase of the kinase family, 1EVF. It also successfullypredicted the catalytic residues for 3HRC despite the lack of information for a relevantside chain atom in the PDB file.