GPCR Structure-Based Virtual Screening Approach for CB2 Antagonist Search

Abstract

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

From Receptors to Ligands: Fragment-assisted Drug Design for GPCRs Applied to the Discovery of H3 and H4 Receptor Antagonists

“…The results reported here are comparable to those from other similar works [19][20][21][22] which showed that GPCR modeling [23][24][25][26][27][28][29][30][31] in the absence of a crystal structure can be a valid replacement [32][33][34][35][36][37][38][39] for structural and functional exploration of GPCR receptors, and for the discovery [21,[40][41][42][43], VS [44][45][46][47][48][49][50][51][52] and optimisation [23,53] of their ligands.…”

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

“…Similarly, using molecular databases spiked with known binders, a number of studies based on combinations of different scoring functions (such as Gold [68], Dock [69], CScore (Tripos), Fresno [70], Score [71], FlexX [72], and PMF [66]) have demonstrated the applicability of molecular docking at GPCR models to virtual screening [9,10,73]. In agreement with the expectations set by these studies, a variety of docking programs and scoring functions have been successfully applied to the discovery of novel ligands for a plethora of GPCRs, including neurorokinin [74,75], adrenergic [76], chemokine [75,77], dopamine [75], serotonin [75,78], cannabinoid [79], and free fatty acid receptors [80], among others.…”

Beta-caryophyllene is a dietary cannabinoid

“…Computational docking analyses, using Surflex-Dock calculations (see SI Materials and Methods) with an established CB 2 receptor homology model (23), suggest that (E)-BCP binds into the hydrophobic region of the water-accessible cavity. The (E)-BCP-CB 2 complex energy was minimized by MD/MM simulations.…”