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