From Water Networks to Binding Affinities:Resolving Solvation Dynamics for Accurate Protein-Ligand Predictions

Water molecules play a critical role in mediating protein-ligand interactions by forming bridging hydrogen bonds and contributing to ligand solvation. However, their intricate behavior, such as frequent exchange with bulk solvent or persistent stabilization in the binding site, makes the accurate binding free-energy estimation via molecular dynamics-based approaches challenging. Particularly, inadequate sampling of water reorganization might not only bias computed affinities but also obscure key interactions, making adequate rehydration of the binding site violated upon calculations. To address this, we employ the polarizable AMOEBA force field together with Lambda-ABF-OPES, an integrated enhanced-sampling framework, which combines lambda-dynamics, multiple-walker adaptive biasing force, and exploratory version of on-the-fly probability enhanced sampling technique that enables efficient rehydration sampling of the binding site without explicitly including any water-related collective variable. Such strategy ensures robust sampling of water exchange and reorganization, enabling reliable rehydration of the binding pose throughout the calculation. Applied to five water-containing protein-ligand complexes with diverse ligand types and binding-site environments, the approach yields binding affinities efficiently and in good agreement with experimental data, demonstrating that Lambda-ABF-OPES captures dynamic water networks and provides robust and reproducible absolute binding free-energy estimation towards chemical accuracy.