Molecular mechanism of ligand induced regulation of cannabinoid receptors

The endocannabinoid signaling system is an attractive target for drug design as it regulates a multitude of physiological and psychological responses by maintaining homeostasis in neuron signaling. This complex system consists of neurotransmitter endocannabinoid molecules and transmembrane cannabinoid receptors where the endocannabinoids bind and lead to downstream signaling and enzymes that help synthesize and degrade endocannabinoids. Diverse functionality and high druggability due to the membrane expression make cannabinoid receptors important drug targets. However, drug discovery efforts targeting these receptors have mostly failed due to the dangerous side effects associated with the receptors, such as anxiety, suicidal tendencies, and euphoria. In addition, synthetic cannabinoids become popular as abusive psychoactive street drugs due to chemical diversity, which helps to avoid detection. Most of the side effects associated with cannabinoid drugs are majorly due to (1) lack of subtype selectivity for drug molecules targeting these receptors, (2) overstimulated response associated with full agonists and antagonists, (3) downstream functional effects of the designed ligands. Therefore, a mechanistic understanding of receptor dynamics and protein-ligand interactions will assist in developing selective therapeutical drugs. With the advent of GPU-accelerated computing and data-driven analysis techniques, Molecular dynamics become an essential tool to study protein dynamics and functions with an unparalleled spatial-temporal resolution. In this thesis, I have utilized computational tools such as molecular dynamics (MD) and deep learning to structurally and energetically explain the lack of ligand selectivity and bias of cannabinoid receptor ligands and explore alternative approaches for drug development. Majorly, I have focused on (1) elucidating partial agonism, (2) exploring the binding of the allosteric sodium ions, (3) explaining subtype ligand selectivity for CB1 and CB2 (4) explaining the molecular mechanism of downstream bias. Collectively, over two milliseconds of molecular dynamics reveal important distinctions in orthosteric and allosteric binding sites, aiding the subtype-selective drug design for cannabinoid receptors. Furthermore, functionally important dynamic protein-ligand interactions were revealed, assisting the development of functionally selective drugs. Overall, this thesis provides a broad mechanistic understanding of protein-ligand interactions and downstream effects of cannabinoid receptors by integrating physics-based and data-driven techniques.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Dutta, Soumajit

Contributors dc:contributor

• Shukla, Diwakar
• Zhao, Huimin
• Mironenko, Alex
• Das, Aditi

• Cannabinoid Receptors
• G-protein Coupled Receptors
• Molecular Dynamics
• Markov State Model
• Deep Learning

Statement dc:rights

• Copyright 2023 Soumajit Dutta

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/122230

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F122230