Details

Autor(en) / Beteiligte

• Eo, H.S. (Seoul National University, Seoul, Republic of Korea)
• Kim, S.M. (Seoul National University, Seoul, Republic of Korea)
• Koo, H.Y. (Sangji University, Wonju, Republic of Korea)
• Kim, W. (Seoul National University, Seoul, Republic of Korea), E-mail: wonkim@plaza.snu.ac.kr

Titel

A machine learning based method for the prediction of G protein-coupled receptor-binding PDZ domain proteins

Ist Teil von

• Molecules and Cells, 2009, 27(6), , pp.629-634

Ort / Verlag

Springer: Korean Society for Molecular and Cellular Biology

Erscheinungsjahr

2009

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• 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.