Title of article
Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms
Author/Authors
Seo, Sangmin Department of Computer Science and Engineering - Incheon National University - Incheon, Republic of Korea , Choi, Jonghwan Department of Computer Science and Engineering - Incheon National University - Incheon, Republic of Korea , l Ahn, Soon Ki Department of Life Science - Incheon National University - Incheon, Republic of Korea , Kim, Kil Won Department of Life Science - Incheon National University - Incheon, Republic of Korea , Kim, Jaekwang Department of Life Science - Incheon National University - Incheon, Republic of Korea , Choi, Jaehyuck Department of Life Science - Incheon National University - Incheon, Republic of Korea , Kim, Jinho Department of Chemistry - Incheon National University - Incheon, Republic of Korea , Ahn, Jaegyoon Department of Computer Science and Engineering - Incheon National University - Incheon, Republic of Korea
Pages
5
From page
1
To page
5
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 efective in predicting GPCR-ligand
binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligandreceptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported
by several studies.
Keywords
GPCR-Ligand , Algorithms , 3D
Journal title
Computational and Mathematical Methods in Medicine
Serial Year
2018
Full Text URL
Record number
2611235
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