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

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.