The cannabinoid (CB) receptors (CB1R and CB2R) represent a promising therapeutic target for several indications such as nociception and obesity. The ligands with nonselectivity can be traced to the high similarity in the binding sites of both cannabinoid receptors. Therefore, the need for selectivity, potency, and G-protein coupling bias has further complicated the design of desired compounds. The bias of currently studied cannabinoid agonists is seldom investigated, and agonists that do exhibit bias are typically nonselective. However, certain long-chain endocannabinoids represent a class of selective and potent CB1R agonists. The binding mode for this class of compounds has remained elusive, limiting the implementation of its binding features to currently studied agonists. Hence, in the present study, the binding poses for these long-chain cannabinoids, along with other interesting ligands, with the receptors have been determined, by using a combination of molecular docking and molecular dynamics (MD) simulations along with molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The binding poses for the long-chain cannabinoids implicate that a site surrounded by the transmembrane (TM)2, TM7, and extracellular loop (ECL)2 is vital for providing the long-chain ligands with the selectivity for CB1R, especially I267 of CB1R (corresponding to L182 of CB2R). Based on the obtained binding modes, the calculated relative binding free energies and selectivity are all in good agreement with the corresponding experimental data, suggesting that the determined binding poses are reasonable. The computational strategy used in this study may also prove fruitful in applications with other GPCRs or membrane-bound proteins.
|Number of pages||9|
|Journal||ACS Chemical Neuroscience|
|State||Published - Oct 21 2020|
Bibliographical noteFunding Information:
This work was supported in part by the funding of the Molecular Modeling and Biopharmaceutical Center at the University of Kentucky College of Pharmacy, the National Science Foundation (NSF Grant CHE-1111761), and the National Institutes of Health (P20 GM130456, UL1TR001998, T32 DA016176, and R01DA039143). The authors also acknowledge the Computer Center at University of Kentucky for supercomputing time on a Dell Supercomputer Cluster consisting of 388 nodes or 4816 processors.
© 2020 American Chemical Society.
- Cannabinoid receptor
- drug design strategy
ASJC Scopus subject areas
- Cognitive Neuroscience
- Cell Biology