Apurba Bhattarai1, Jinan Wang1, Yinglong Miao2. 1. Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA. 2. Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS 66047, USA. Electronic address: miao@ku.edu.
Abstract
BACKGROUND: Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR. METHODS: Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR. RESULTS: Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased. CONCLUSIONS: Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking. GENERAL SIGNIFICANCE: Ensemble docking is a promising approach for drug discovery targeting flexible receptors.
BACKGROUND: Ensemble docking has proven useful in drug discovery and development. It increases the hit rate by incorporating receptor flexibility into molecular docking as demonstrated on important drug targets including G-protein-coupled receptors (GPCRs). Adenosine A1 receptor (A1AR) is a key GPCR that has been targeted for treating cardiac ischemia-reperfusion injuries, neuropathic pain and renal diseases. Development of allosteric modulators, compounds binding to distinct and less conserved GPCR target sites compared with agonists and antagonists, has attracted increasing interest for designing selective drugs of the A1AR. Despite significant advances, more effective approaches are needed to discover potent and selective allosteric modulators of the A1AR. METHODS: Ensemble docking that integrates Gaussian accelerated molecular dynamic (GaMD) simulations and molecular docking using Autodock has been implemented for retrospective docking of known positive allosteric modulators (PAMs) in the A1AR. RESULTS: Ensemble docking outperforms docking of the receptor cryo-EM structure. The calculated docking enrichment factors (EFs) and the area under the receiver operating characteristic curves (AUC) are significantly increased. CONCLUSIONS: Receptor ensembles generated from GaMD simulations are able to increase the success rate of discovering PAMs of A1AR. It is important to account for receptor flexibility through GaMD simulations and flexible docking. GENERAL SIGNIFICANCE: Ensemble docking is a promising approach for drug discovery targeting flexible receptors.
Authors: C Elisabet Tranberg; Andrea Zickgraf; Brian N Giunta; Henning Luetjens; Heidi Figler; Lauren J Murphree; Ruediger Falke; Holger Fleischer; Joel Linden; Peter J Scammells; Ray A Olsson Journal: J Med Chem Date: 2002-01-17 Impact factor: 7.446
Authors: P G Baraldi; A N Zaid; I Lampronti; F Fruttarolo; M G Pavani; M A Tabrizi; J C Shryock; E Leung; R Romagnoli Journal: Bioorg Med Chem Lett Date: 2000-09-04 Impact factor: 2.823
Authors: K Vanommeslaeghe; E Hatcher; C Acharya; S Kundu; S Zhong; J Shim; E Darian; O Guvench; P Lopes; I Vorobyov; A D Mackerell Journal: J Comput Chem Date: 2010-03 Impact factor: 3.376
Authors: Pradeep Anand Ravindranath; Stefano Forli; David S Goodsell; Arthur J Olson; Michel F Sanner Journal: PLoS Comput Biol Date: 2015-12-02 Impact factor: 4.475