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Epilepsy is a disease caused by abnormal brain activity due to disturbed nerve cell activity. It is the fourth most common neurological disorder. Only about 7 out of 10 individuals with epilepsy are successfully treated using anti-epileptic drugs. In the pharmaceutical industry, there has been a growing demand for cannabinoids from Cannabis sativa, commonly known as marijuana, for therapeutic, clinical, and nutraceutical supplements. More recently, the legalization of marijuana for clinical research has allowed to further explore the efficacy in the treatment of several neurological disorders like epilepsy. Unlike opioids, cannabinoids are not psychoactive, making it a potentially more favorable therapeutic drug. Most studies showing the efficacy of Cannabis as a treatment strategy point to the pain management associated with the binding of endocannabinoid G coupled protein receptors CB1 and CB2. Though there are cannabinoid therapeutic drugs like Epidiolex approved by the Food and Drug Administration (FDA), plant-based natural compounds are safer, and effective with no side effects.
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Characterization of binding kinetics and intracellular signaling of new psychoactive substances targeting cannabinoid receptor using transition-based reweighting method
New psychoactive substances (NPS) targeting cannabinoid receptor 1 pose a significant threat to society as recreational abusive drugs that have pronounced physiological side effects. These greater adverse effects compared to classical cannabinoids have been linked to the higher downstream β-arrestin signaling. Thus, understanding the mechanism of differential signaling will reveal important structure-activity relationship essential for identifying and potentially regulating NPS molecules. In this study, we simulate the slow (un)binding process of NPS MDMB-Fubinaca and classical cannabinoid HU-210 from CB1 using multi-ensemble simulation to decipher the effects of ligand binding dynamics on downstream signaling. The transition-based reweighing method is used for the estimation of transition rates and underlying thermodynamics of (un)binding processes of ligands with nanomolar affinities. Our analyses reveal major interaction differences with transmembrane TM7 between NPS and classical cannabinoids. A variational autoencoder-based approach, neural relational inference (NRI), is applied to assess the allosteric effects on intracellular regions attributable to variations in binding pocket interactions. NRI analysis indicate a heightened level of allosteric control of NPxxY motif for NPS-bound receptors, which contributes to the higher probability of formation of a crucial triad interaction (Y7.53-Y5.58-T3.46) necessary for stronger β-arrestin signaling. Hence, in this work, MD simulation, data-driven statistical methods, and deep learning point out the structural basis for the heightened physiological side effects associated with NPS, contributing to efforts aimed at mitigating their public health impact.
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- Award ID(s):
- 1845606
- PAR ID:
- 10587204
- Publisher / Repository:
- eLife
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Posted Content:
- https://doi.org/10.7554/eLife.98798.1
