Improving Binding Free Energy Predictions with Swap Monte Carlo for Water Sampling

Area of Science:

• Computational chemistry
• Molecular dynamics simulations

Background:

• Water molecules in binding cavities significantly influence ligand binding affinity.
• Accurate sampling of these water molecules is crucial for reliable free energy calculations.

Purpose of the Study:

• To develop a novel and efficient method for sampling cavity water molecules.
• To improve the accuracy of binding free energy calculations by enhancing water distribution exploration.

Main Methods:

• Developed the Swap Monte Carlo (SwapMC) method for cavity water sampling.
• Utilized GPU acceleration for parallel Monte Carlo moves.
• Integrated SwapMC with NPT simulations in the Uni-FEP framework.

Main Results:

• SwapMC significantly improved the accuracy of relative binding free energy calculations.
• The method demonstrated computational efficiency comparable to Grand Canonical Monte Carlo (GCMC).
• Achieved comprehensive exploration of water distributions within protein cavities.

Conclusions:

• SwapMC provides a robust and efficient alternative for cavity water sampling.
• The method enhances the accuracy of molecular dynamics-based binding free energy predictions.
• Addresses key challenges in accurately modeling water's role in molecular recognition.