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GPCR structure-based virtual screening approach for CB2 antagonist search.
J Chem Inf Model. 2007 Jul-Aug; 47(4):1626-37.JC

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.

Authors+Show Affiliations

Department of Pharmaceutical Sciences, School of Pharmacy, Pittsburgh Molecular Library Screening Center, Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.No affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

17580929

Citation

Chen, Jian-Zhong, et al. "GPCR Structure-based Virtual Screening Approach for CB2 Antagonist Search." Journal of Chemical Information and Modeling, vol. 47, no. 4, 2007, pp. 1626-37.
Chen JZ, Wang J, Xie XQ. GPCR structure-based virtual screening approach for CB2 antagonist search. J Chem Inf Model. 2007;47(4):1626-37.
Chen, J. Z., Wang, J., & Xie, X. Q. (2007). GPCR structure-based virtual screening approach for CB2 antagonist search. Journal of Chemical Information and Modeling, 47(4), 1626-37.
Chen JZ, Wang J, Xie XQ. GPCR Structure-based Virtual Screening Approach for CB2 Antagonist Search. J Chem Inf Model. 2007 Jul-Aug;47(4):1626-37. PubMed PMID: 17580929.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - GPCR structure-based virtual screening approach for CB2 antagonist search. AU - Chen,Jian-Zhong, AU - Wang,Junmei, AU - Xie,Xiang-Qun, Y1 - 2007/06/20/ PY - 2007/6/22/pubmed PY - 2007/10/2/medline PY - 2007/6/22/entrez SP - 1626 EP - 37 JF - Journal of chemical information and modeling JO - J Chem Inf Model VL - 47 IS - 4 N2 - 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. SN - 1549-9596 UR - https://www.unboundmedicine.com/medline/citation/17580929/GPCR_structure_based_virtual_screening_approach_for_CB2_antagonist_search_ L2 - https://doi.org/10.1021/ci7000814 DB - PRIME DP - Unbound Medicine ER -