Negative allosteric modulators of cannabinoid receptor 2: protein modeling, binding site identification and molecular dynamics simulations in the presence of an orthosteric agonist

Abstract

Selective activation of the cannabinoid receptor subtype 2 (CB2) shows promise for treating pain, inflammation, multiple sclerosis, cancer, ischemic/reperfusion injury and osteoporosis. Target selectivity and off-target side effects are two major limiting factors for orthosteric ligands, and therefore, the search for allosteric modulators (AMs) is a widely used drug discovery approach. To date, only a limited number of negative CB2 AMs have been identified, possessing only micromolar activity at best, and the CB2 receptor’s allosteric site(s) are not well characterized. Herein, we used computational approaches including receptor modeling, site mapping, docking, molecular dynamics (MD) simulations and binding free energy calculations to predict, characterize and validate allosteric sites within the complex of the CB2 receptor with bound orthosteric agonist CP55,940. After docking of known negative CB2 allosteric modulators (NAMs), dihydro-gambogic acid (DHGA) and trans-β-caryophyllene (TBC) (note that TBC also shows agonist activity), at the predicted allosteric sites, the best total complex with CB2, CP55,940 and NAM was embedded into a hydrated lipid bilayer and subjected to a 200 ns MD simulation. The presence of an AM affected the CB2–CP55,940 complex, altering the relative positioning of the toggle switch residues and promoting a strong π–π interaction between Phe117^3.36 and Trp258^6.48. Binding of either TBC or DHGA to a putative allosteric pocket directly adjacent to the orthosteric ligand reduced the binding free energy of CP55,940, which is consistent with the expected effect of a negative AM. The identified allosteric sites present immense scope for the discovery of novel classes of CB2 AMs.

Keywords:

• CB2 receptor
• CB2 allosteric modulator
• docking
• G-protein-coupled receptor
• molecular dynamics

Disclosure statement

The authors declare that they have no competing interests.

Additional information

Funding

This study was supported by an Institutional Development Award (IDeA) Grant Number P20GM104932 and by R15GM119061 from the National Institute of General Medical Sciences (NIGMS), a component of the National Institutes of Health (NIH). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIGMS or NIH. This study was also supported by a National Science Foundation (NSF) Major Research Infrastructure Grant entitled ‘MRI: Acquisition of a GPU Cluster for Computational Science in Mississippi’, Award #CHE-1338056, and an Extreme Science and Engineering Discovery Environment (XSEDE) grant of supercomputer time, MCB140248. It was conducted in part in a facility constructed with support from the Research Facilities Improvements Program (C06 RR-14503) from NIH National Center for Research Resources.