DOI: 10.5061/dryad.4f4qrfjq5
title: Data from: Characterization of binding kinetics and intracellular signaling of new psychoactive substances targeting cannabinoid receptor using transition-based reweighting method

description: 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
 relationships 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 indicates 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.
description: # Characterization of binding kinetics and intracellular signaling of new
 psychoactive substances targeting cannabinoid receptor using
 transition-based reweighting method
 [https://doi.org/10.5061/dryad.4f4qrfjq5](https://doi.org/10.5061/dryad.4f4qrfjq5) ## Description of the data and file structure This README file was generated on 2025-03-02 by Soumajit Dutta #### Contributors * Soumajit Dutta : Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign * Diwakar Shukla (Corresponding Author) : Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign #### Overview The dataset contains molecular dynamics (MD) trajectories obtained using various simulation techniques for the associated research paper, along with the files containing corresponding parameter and topology. All simulations employed the CHARMM36m force field for proteins, while synthetic cannabinoids were parameterized using the CGenFF force field. Well-Tempered Metadynamics simulations were conducted with NAMD 2.14 using the Colvars module. Additionally, unbiased simulations and umbrella sampling were performed with OpenMM v7.8. ### Files and variables #### File: CB1\_nps\_Well-tempered\_metadynamics.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.psf) for NPS unbinding simulation using well-tempered metadynamics method. #### File: CB1\_classical\_Well-tempered\_metadynamics.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.psf) for classical cannabinoid unbinding simulation using well-tempered metadynamics method. #### File: CB1\_nps\_Umbrella\_sampling.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.prmtop) for NPS (un)binding simulation using umbrella sampling method. #### File: CB1\_classical\_Umbrella\_sampling.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.prmtop) for classical cannabinoid (un)binding simulation using umbrella sampling method. #### File: CB1\_nps\_Unbiased.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.prmtop) for unbiased NPS (un)binding simulation. #### File: CB1\_classical\_Unbiased.tar.gz **Description:** This compressed file contains trajectories (.dcd) and topology (and parameter) (.prmtop) for unbiased classical cannabinoid (un)binding simulation. ## Code/software VMD software can be used to visualize the trajectory files. The python codes to regenerate the analysis figures in main text is provided in our Github Repository: [https://github.com/ShuklaGroup/Dutta_Shukla_Cannabinoid_2023a.git](https://github.com/ShuklaGroup/Dutta_Shukla_Cannabinoid_2023a.git) For further inquiries concerning further data availability, please contact Prof. Diwakar Shukla at [diwakar@illinois.edu](mailto:diwakar@illinois.edu).