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Mol. Cells 2009; 27(6): 629-634

Published online June 30, 2009

https://doi.org/10.1007/s10059-009-0091-2

© The Korean Society for Molecular and Cellular Biology

A Machine Learning Based Method for thePrediction of G Protein-Coupled Receptor-Binding PDZ Domain Proteins

Hae-Seok Eo, Sungmin Kim, Hyeyoung Koo, and Won Kim

Received: January 13, 2009; Revised: May 1, 2009; Accepted: May 12, 2009

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 identi-fication 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 G protein-coupled receptor, GPCR interacting protein, machine learning, PDZ domain, support vector machine

Article

Research Article

Mol. Cells 2009; 27(6): 629-634

Published online June 30, 2009 https://doi.org/10.1007/s10059-009-0091-2

Copyright © The Korean Society for Molecular and Cellular Biology.

A Machine Learning Based Method for thePrediction of G Protein-Coupled Receptor-Binding PDZ Domain Proteins

Hae-Seok Eo, Sungmin Kim, Hyeyoung Koo, and Won Kim

Received: January 13, 2009; Revised: May 1, 2009; Accepted: May 12, 2009

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 identi-fication 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: G protein-coupled receptor, GPCR interacting protein, machine learning, PDZ domain, support vector machine

Mol. Cells
Jan 31, 2023 Vol.46 No.1, pp. 1~67
COVER PICTURE
RNAs form diverse shapes and play multiple functions as central molecules of gene expression. In this special issue on RNA, seven minireviews illustrate how basic concepts and recent RNA biology findings are transformed into new and exciting RNA therapeutics.

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