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

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

    Research output: Contribution to conferencePaperpeer-review

    3 Scopus citations

    Abstract

    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)
    Pages938-945
    Number of pages8
    DOIs
    StatePublished - Dec 1 2007
    EventProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, USA, Boston, United States
    Duration: Oct 14 2007Oct 17 2007

    Conference

    ConferenceProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
    Country/TerritoryUnited States
    CityBoston
    Period10/14/0710/17/07

    All Science Journal Classification (ASJC) codes

    • Immunology

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