Activation mechanisms of non-class A G protein-coupled receptors

Bansal, Prateek

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https://hdl.handle.net/2142/132780 Copy

Description

Title

Activation mechanisms of non-class A G protein-coupled receptors

Author(s)

Bansal, Prateek

Issue Date

2025-11-30

Director of Research (if dissertation) or Advisor (if thesis)

Shukla, Diwakar

Doctoral Committee Chair(s)

Shukla, Diwakar

Committee Member(s)

• Zhao, Huimin
• Schroeder, Charles M.
• Pogorelov, Taras

Department of Study

Chemical & Biomolecular Engr

Discipline

Chemical Engineering

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• GPCR
• SMO
• GCGR
• Activation
• Mechanism
• GLP1R
• PAC1R
• PTH1R
• STE2
• SCTR
• CALCR
• Molecular Dynamics

Abstract

G protein-coupled receptors are central to mediating homeostasis across a plethora of biochemical signaling pathways across the entire body and are one of the most important classes of drug targets. While the majority of the recent literature on GPCRs focuses on Class A GPCRs, the structural and dynamic insights underlying the activation mechanism of non-Class A GPCRs are lacking. This thesis addresses this gap by investigating activation mechanisms of Class F receptor Smoothened (SMO), six class B1 GPCRs - Glucagon receptor (GCGR), Glucagon-like Peptide 1 receptor (GLP1R), Parathyroid Hormone 1 receptor (PTH1R), Secretin Receptor (SCTR) and Pituitary Adenylate Cyclase 1 receptor (PAC1R), and fungal Class D receptor STE2, using large-scale all atom unbiased molecular dynamics simulations. To overcome the limitations associated with traditional MD simulations, we employ multiple adaptive sampling strategies to sample rare events faster. To overcome the sampling bias introduced by adaptive sampling, we construct Markov State Models and reweigh the probabilities of the sampled free energy landscapes. These approaches allow us to reconstruct the full activation landscape for each of these proteins and recover the thermodynamics and kinetics associated with the state transitions. In the case of Class F receptor SMO, we characterize its basal activity from a ligand-free and ligand-bound mode, and show that SMO activation is mediated through a conserved motif unique to Class F receptors. Additionally, we also probe the endogenous activation mechanism of SMO by investigating the feasibility of the translocation of a molecule of cholesterol through its hydrophobic channel. Using a combination of molecular dynamics and experimental mutagenesis, we show that cholesterol can translocate through SMO through two different modes, which are both energetically feasible. We further comment on the interplay between the two ligand binding sites in SMO, and show how molecules that bind to the extracellular Cysteine Rich Domain (CRD) can act as agonists, while molecules that bind to the transmembrane domain can act as antagonists, by identifying a conserved residue that accesses different rotameric conformations based on the binding site occupied. For Class B1 GPCRs, we show that the activation mechanism involves a conserved outward displacement of TM6 followed by kinking. In addition to this universal conserved activation, we also show that Class B1 GPCRs show clade-specific locks that are broken on activation, further demonstrating a potential for MD simulations to identify crucial intermediate states specific to certain clades that can be targeted for therapeutic design. Finally, for Class D GPCR STE2, we show that the protein follows a non-canonical activation mechanism, and that a conserved serine residue in TM7 plays a crucial role in mediating the activation process. By combining simulation trajectories spanning over 10 milliseconds with machine-learning-driven state decomposition, we reveal conserved mechanistic motions across an entire collection of families of GPCRs that are understudied. Collectively, this thesis contributes a detailed and comparative atomistic understanding of GPCR activation beyond Class A GPCRs, and highlights the power of unbiased MD and data-driven modeling to uncover the activity of these previously poorly understood proteins.

Graduation Semester

2025-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/132780

Copyright and License Information

Copyright 2025 Prateek Bansal

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