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Characterizing and Predicting Catalytic Residues in Enzyme Active Sites Based on Local Properties: A Machine Learning Approach

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4 Author(s)
Leonardo Bobadilla ; Department of Statistics, National University of Colombia, jlbobadillam@unal.edu.co ; Fernando Nino ; Edilberto Cepeda ; Manuel A. Patarroyo

Developing computational methods for assigning protein function from tertiary structure is a very important problem, predicting a catalytic mechanism based only on structural information being a particularly challenging task. This work focuses on helping to understand the molecular basis of catalysis by exploring the nature of catalytic residues, their environment and characteristic properties in a large data set of enzyme structures and using this information to predict enzyme structures' active sites. A machine learning approach that performs feature extraction, clustering and classification on a protein structure data set is proposed. The 6,376 residues directly involved in enzyme catalysis, present in more than 800 proteins structures in the PDB were analyzed. Feature extraction provided a description of critical features for each catalytic residue, which were consistent with prior knowledge about them. Results from k-fold-cross-validation for classification showed more than 80% accuracy. Complete enzymes were scanned using these classifiers to locate catalytic residues.

Published in:

2007 IEEE 7th International Symposium on BioInformatics and BioEngineering

Date of Conference:

14-17 Oct. 2007