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

Abstract

Computational approaches for predicting the binding affinity of ligand–receptor complex structures often fail to validate experimental results satisfactorily due to insufficient sampling. To address these challenges, recent emphasis has been placed on the re-sampling of new trajectories. In this study, we propose a simulation protocol that achieves efficient sampling by re-engineering the widely used Bennett acceptance ratio (BAR) method as a representative approach. We tested its efficacy across various membrane protein targets, including G-protein coupled receptors (GPCRs) with diverse structural landscapes and experimentally validated binding affinities, to verify its efficient applicability. Subsequently, using BAR-based binding free energy calculations, we confirmed correlations with experimental data, demonstrating the validity and performance of this computational approach.