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Endocannabinoids are naturally occurring lipid-like molecules that bind to cannabinoid receptors (CB₁and CB₂) and regulate many of human bodily functions via the endocannabinoid system. There is a tremendous interest in developing selective drugs that target the CB receptors. However, the biophysical mechanisms responsible for the subtype selectivity for endocannbinoids have not been established. Recent experimental structures of CB receptors show that endocannbinoids potentially bind via membrane using the lipid access channel in the transmembrane region of the receptors. Furthermore, the N-terminus of the receptor could move in and out of the binding pocket thereby modulating both the pocket volume and its residue composition. On the basis of these observations, we propose two hypothesis to explain the selectivity of the endocannabinoid, anandamide for CB₁receptor. First, the selectivity arises from distinct enthalpic ligand-protein interactions along the ligand binding pathway formed due to the movement of N-terminus and subsequent shifts in the binding pocket composition. Second, selectivity arises from the volumetric differences in the binding pocket allowing for differences in ligand conformational entropy. To quantitatively test these hypothesis, we perform extensive molecular dynamics simulations (∼0.9 milliseconds) along with Markov state modeling and deep learning-based VAMPnets to provide an interpretable characterization of the anandamide binding process to cannabinoid receptors and explain its selectivity for CB₁. Our findings reveal that the distinct N-terminus positions along lipid access channels between TM1 and TM7 lead to different binding mechanisms and interactions between anandamide and the binding pocket residues. To validate the critical stabilizing interactions along the binding pathway, relative free energy calculations of anandamide analogs are used. Moreover, the larger CB₂pocket volume increases the entropic effects of ligand binding by allowing higher ligand fluctuations but reduced stable interactions. Therefore, the opposing enthalpy and entropy effects between the receptors shape the endocannabinoid selectivity. Overall, the CB₁selectivity of anandamide is explained by the dominant enthalpy contributions due to ligand-protein interactions in stable binding poses. This study shed lights on potential selectivity mechanisms for endocannabinoids that would aid in the discovery of CB selective drugs 

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. 

Reconciliation of experimental and computational spectroscopic observables is critical for understanding protein dynamics.