grand: A Python Module for Grand Canonical Water Sampling in OpenMM.

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Water molecules at protein-ligand interfaces are crucial for drug-target interactions.
• Conventional molecular dynamics (MD) methods struggle to adequately sample these water molecules due to long equilibration times.
• Grand canonical methods offer a solution by allowing water insertion/deletion based on chemical potential.

Purpose of the Study:

• To introduce an open-source Python module, 'grand', for integrating grand canonical Monte Carlo (GCMC) sampling with MD simulations.
• To enable more efficient and accurate sampling of water molecules in complex biological systems.
• To improve the understanding of water's role in drug-target interactions.

Main Methods:

• Developed an open-source Python module named 'grand'.
• Integrated GCMC sampling with MD simulations using the OpenMM engine.
• Validated the module by simulating bulk water density and analyzing water sites in a protein.

Main Results:

• The 'grand' module accurately reproduces the density of bulk water from constant pressure simulations.
• Simulations using 'grand' successfully identified three buried crystallographic water sites in the bovine pancreatic trypsin inhibitor protein.
• These water sites were previously poorly sampled by conventional MD methods.

Conclusions:

• The 'grand' module provides an effective tool for enhanced sampling of water molecules in MD simulations.
• This approach significantly improves the characterization of water networks at protein-ligand interfaces.
• The module has the potential to advance drug discovery by providing more accurate insights into drug-target binding.