Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.

TY - JOUR

T1 - Mutation-Guided Unbiased Modeling of the Fat Sensor GPR119 for High-Yield Agonist Screening.

AU - Norn, Christoffer

AU - Pedersen, Maria Hauge

AU - Engelstoft, Maja S.

AU - Kim, Sun Hee

AU - Lehmann, Juerg

AU - Jones, Robert M.

AU - Schwartz, Thue W.

AU - Frimurer, Thomas M.

N1 - M1 - Copyright (C) 2015 American Chemical Society (ACS). All Rights Reserved. CAPLUS AN 2015:1756764(Journal; Online Computer File)

PY - 2015/12/1

Y1 - 2015/12/1

N2 - Summary Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

AB - Summary Recent benchmark studies have demonstrated the difficulties in obtaining accurate predictions of ligand binding conformations to comparative models of G-protein-coupled receptors. We have developed a data-driven optimization protocol, which integrates mutational data and structural information from multiple X-ray receptor structures in combination with a fully flexible ligand docking protocol to determine the binding conformation of AR231453, a small-molecule agonist, in the GPR119 receptor. Resulting models converge to one conformation that explains the majority of data from mutation studies and is consistent with the structure-activity relationship for a large number of AR231453 analogs. Another key property of the refined models is their success in separating active ligands from decoys in a large-scale virtual screening. These results demonstrate that mutation-guided receptor modeling can provide predictions of practical value for describing receptor-ligand interactions and drug discovery.

U2 - 10.1016/j.str.2015.09.014

DO - 10.1016/j.str.2015.09.014

M3 - Journal article

SN - 0969-2126

VL - 23

SP - 2377

EP - 2386

JO - Structure

JF - Structure

IS - 12

ER -