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Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

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.

SUBMITTER: Seo S 

PROVIDER: S-EPMC5831789 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Seo Sangmin S   Choi Jonghwan J   Ahn Soon Kil SK   Kim Kil Won KW   Kim Jaekwang J   Choi Jaehyuck J   Kim Jinho J   Ahn Jaegyoon J  

Computational and mathematical methods in medicine 20180130

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 li  ...[more]

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