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

Research output: Contribution to journalJournal articleResearchpeer-review

Christoffer Norn, Maria Hauge Pedersen, Maja S. Engelstoft, Sun Hee Kim, Juerg Lehmann, Robert M. Jones, Thue W. Schwartz, Thomas M. Frimurer

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 det. the binding conformation of AR231453, a small-mol. 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 no. of AR231453 analogs. Another key property of the refined models is their success in sepg. 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.
Original languageEnglish
Issue number12
Pages (from-to)2377-2386
Number of pages10
Publication statusPublished - 2015

Bibliographical note

M1 - Copyright (C) 2015 American Chemical Society (ACS). All Rights Reserved.

CAPLUS AN 2015:1756764(Journal; Online Computer File)

ID: 150703222