Molecules and Cells

Korean Society for Molecular and Cellular Biology (한국분자세포생물학회)
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A Machine Learning Based Method for the Prediction of G Protein-Coupled Receptor-Binding PDZ Domain Proteins

  • Eo, Hae-Seok (School of Biological Sciences, Seoul National University) ;
  • Kim, Sungmin (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Koo, Hyeyoung (Department of Biological Science, Sangji University) ;
  • Kim, Won (School of Biological Sciences, Seoul National University)
  • Received : 2008.11.13
  • Accepted : 2009.05.12
  • Published : 2009.06.30
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Abstract

G protein-coupled receptors (GPCRs) are part of multi-protein networks called 'receptosomes'. These GPCR interacting proteins (GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed the identification of novel GPCR-PDZ domain protein interactions.

Keywords

Acknowledgement

Supported by : Korea Research Foundation

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