Ligand Dissociation Pathways from Membrane Receptors Revealed by Weighted Ensemble Simulations

Area of Science:

• Biochemistry and Molecular Pharmacology
• Computational Biophysics

Background:

• G-protein-coupled receptors (GPCRs) are crucial for cell signaling and are major drug targets.
• β-adrenergic receptors (β₁AR and β₂AR) control cardiovascular function and are activated by norepinephrine (NE) and epinephrine (EP).
• Understanding ligand dissociation kinetics (k_off) is vital for drug development, but atomistic modeling is challenging due to slow unbinding events.

Purpose of the Study:

• To apply the weighted ensemble (WE) method with molecular dynamics (MD) to simulate and analyze ligand dissociation pathways from β₁AR and β₂AR.
• To utilize machine learning (ML) techniques for unbiased analysis and visualization of dissociation processes.
• To accurately determine dissociation rate constants (k_off) for NE and EP from these receptors.

Main Methods:

• Employed the weighted ensemble (WE) method combined with molecular dynamics (MD) simulations.
• Utilized machine learning algorithms including principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and DBSCAN for data analysis.
• Simulated the dissociation of norepinephrine (NE) and epinephrine (EP) from membrane-embedded β₁AR and β₂AR.

Main Results:

• Identified distinct dissociation pathways for NE and EP: front-side for β₁AR and back-side for β₂AR.
• Successfully sampled rare ligand unbinding events from GPCRs using WE-MD.
• Demonstrated the capability of WE simulations to provide reliable estimates of dissociation rate constants (k_off).

Conclusions:

• The WE method offers an effective approach for unbiased sampling of ligand dissociation from GPCRs.
• Ligand binding and unbinding mechanisms differ between β₁AR and β₂AR.
• This study enhances our understanding of GPCR-ligand kinetics, crucial for rational drug design.