Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Leonardo Bobadilla, Fernando Niño, Edilberto Cepeda, Manuel A. Patarroyo

Research output: Contribution to conferencePaper

3 Scopus citations


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 performsfeature extraction, clustering and classification on a protein structure data set is proposed. 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. ©2007 IEEE.
Original languageEnglish (US)
Number of pages8
StatePublished - Dec 1 2007
Externally publishedYes
Eventconference -
Duration: Dec 1 2007 → …


Period12/1/07 → …


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