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Virtual Screening of CB 2 Receptor Agonists from Bayesian Network and High‐Throughput Docking: Structural Insights into Agonist‐Modulated GPCR Features
Author(s) -
Renault Nicolas,
Laurent Xavier,
Farce Amaury,
El Bakali Jamal,
Mansouri Roxane,
Gervois Philippe,
Millet Régis,
Desreumaux Pierre,
Furman Christophe,
Chavatte Philippe
Publication year - 2013
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.12095
Subject(s) - virtual screening , agonist , g protein coupled receptor , docking (animal) , computational biology , receptor , inverse agonist , drug discovery , chemistry , high throughput screening , homology modeling , biochemistry , biology , medicine , enzyme , nursing
The relevance of CB 2 ‐mediated therapeutics is well established in the treatment of pain, neurodegenerative and gastrointestinal tract disorders. Recent works such as the crystallization of class‐A G‐protein‐coupled receptors in a range of active states and the identification of specific anchoring sites for CB 2 agonists challenged us to design a reliable agonist‐bound homology model of CB 2 receptor. Docking‐scoring enrichment tests of a high‐throughput virtual screening of 140 compounds led to 13 hits within the micromolar affinity range. Most of these hits behaved as CB 2 agonists, among which two novel full agonists emerged. Although the main challenge was a high‐throughput docking run targeting an agonist‐bound state of a CB 2 model, a prior 2D ligand‐based Bayesian network was computed to enrich the input commercial library for 3D screening. The exclusive discovery of agonists illustrates the reliability of this agonist‐bound state model for the identification of polar and aromatic amino acids as new agonist‐modulated CB 2 features to be integrated in the wide activation pathway of G‐protein‐coupled receptors.