Enhancing binding affinity predictions through efficient sampling with a re-engineered BAR method: a test on GPCR...

Area of Science:

• Computational Chemistry
• Molecular Dynamics
• Structural Biology

Background:

• Predicting ligand-receptor binding affinity computationally is crucial but often limited by insufficient sampling in molecular simulations.
• Existing methods struggle to accurately validate experimental results due to sampling limitations.
• Re-sampling molecular trajectories is a key strategy to overcome these challenges.

Purpose of the Study:

• To propose and validate a novel simulation protocol for efficient molecular sampling.
• To re-engineer the Bennett acceptance ratio (BAR) method for enhanced sampling efficiency.
• To assess the protocol's performance across diverse membrane protein targets, including GPCRs.

Main Methods:

• Re-engineering the Bennett acceptance ratio (BAR) method to improve sampling efficiency.
• Applying the modified BAR method to molecular dynamics simulations of membrane protein targets.
• Testing the protocol on G-protein coupled receptors (GPCRs) with known binding affinities.

Main Results:

• The proposed simulation protocol achieved efficient sampling for complex molecular systems.
• BAR-based binding free energy calculations demonstrated strong correlations with experimental data.
• The method proved effective for various membrane protein targets, including diverse GPCRs.

Conclusions:

• The re-engineered BAR method offers a valid and high-performing computational approach for binding affinity prediction.
• This protocol enhances the accuracy and reliability of molecular simulations for drug discovery and biophysics.
• The study validates the efficient applicability of the enhanced sampling protocol across challenging membrane protein targets.