Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms. (30th January 2018)

• Record Type: Journal Article
• Authors: Seo, Sangmin; Choi, Jonghwan; Ahn, Soon Kil; Kim, Kil Won; Kim, Jaekwang; Choi, Jaehyuck; Kim, Jinho; Ahn, Jaegyoon
• Other Names: Mitchell John Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2018(2018)
• Publication Date: 2018-01-30
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2018/6565241
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.