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Consensus of Sample-Balanced Classifiers for Identifying Ligand-Binding Residue by Co-evolutionary Physicochemical Characteristics of Amino Acids

  • Peng Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 375)

Abstract

Protein-ligand binding is an important mechanism for some proteins to perform their functions, and those binding sites are the residues of proteins that physically bind to ligands. So far, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available. In this paper, we propose a sequence-based approach to identify protein-ligand binding residues. Due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets, for each of which a random forest (RF)-based classifier was trained. The ensemble of these RF classifiers formed a sequence-based protein-ligand binding site predictor. Experimental results on CASP9 targets demonstrated that our method compared favorably with the state-of-the-art.

Keywords

Protein-ligand binding Random forest Co-evolutionary encoding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peng Chen
    • 1
  1. 1.Computer, Electrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia

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