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Unveiling the Functional Dynamics of G Protein-Coupled Receptors through Free-Energy Calculations [Nodes Seminar]

3 set. 25

Speaker: Prof. Vittorio Limongelli

Faculty of Biomedical Sciences, Euler Institute, University of Lugano (USI), Switzerland

 

Imatge

Presentation

Host: Dr. Modesto Orozco, Group Leader - Molecular Modelling and Bioinformatics - IRB Barcelona

Node: Computational Biology / Chemical & Structural Biology

Date: Wednesday 03 September 2025, 12:00h

Place: Auditorium Room - PCB

 

 

Abstract:

Elucidating the functional dynamics of G Protein-Coupled Receptors (GPCRs) is crucial for guiding rational drug discovery [1]. However, the complexity and long timescales of receptor activation make many aspects of GPCR function elusive to both experimental and computational approaches. Here, I demonstrate how these challenges can be overcome using advanced simulation techniques such as funnel-metadynamics [2] and coarse-grained molecular dynamics [3,4], enabling the characterization of thermodynamic and kinetic properties of drug binding, receptor activation, and dimerization.
In particular, I highlight the case of the adenosine A2A receptor, where ligand binding occurs through multiple binding modes, with transitions between the vestibular and orthosteric sites dictating the ligand (un)binding rate. Agonist and antagonist binding allosterically modulate the receptor, shifting it from a pseudo-active state to either a fully active or inactive conformation [5].
Furthermore, receptor dimerization - occurring on the minute timescale - can influence ligand accessibility at the extracellular site and effector G protein binding at the intracellular site. This suggests that dimerization functions as a sophisticated allosteric regulatory mechanism of GPCR activity [6].
The advancement of molecular binding simulation techniques [1] is poised to revolutionize drug discovery by incorporating structural, energetic, and dynamic receptor properties. This approach holds great potential for designing ligands with tailored bias signaling, ultimately leading to more effective therapeutics across a wide range of diseases.

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