GPCR structure-based virtual screening approach for CB2 antagonist search.
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.
Chen
Jian-Zhong
JZ
Department of Pharmaceutical Sciences, School of Pharmacy, Pittsburgh Molecular Library Screening Center, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.
Wang
Junmei
J
Xie
Xiang-Qun
XQ
D008958:Models, Molecular; D011485:Protein Binding; D011487:Protein Conformation; D043885:Receptor, Cannabinoid, CB2 / Q000037:antagonists & inhibitors* / Q000378:metabolism; D043562:Receptors, G-Protein-Coupled / Q000737:chemistry* / Q000378:metabolism
D043885:Receptor, Cannabinoid, CB2; D043562:Receptors, G-Protein-Coupled