Waterdock 2.0: Water placement prediction for Holo-structures with a pymol plugin

Area of Science:

• Computational chemistry and structural biology.
• Molecular modeling and drug design.

Background:

• Water molecules play a crucial role in protein-ligand interactions, influencing binding affinity and orientation.
• Accurate prediction of water molecule positions in binding pockets is essential for understanding and optimizing drug binding.

Purpose of the Study:

• To develop an improved computational protocol for predicting water molecule positions in protein binding pockets.
• To reduce the false-positive rate of water molecule prediction compared to existing methods.
• To enhance the usability of water molecule prediction tools through integration with molecular visualization software.

Main Methods:

• Developed the WaterDock2 protocol, incorporating knowledge of ligand functional group solvation structures.
• Evaluated WaterDock2's performance against the original WaterDock protocol using a test set.
• Created a PyMOL plugin that includes both WaterDock and WaterDock2 implementations.

Main Results:

• WaterDock2 maintains a high true positive rate for predicting water molecules, similar to the original WaterDock.
• WaterDock2 achieves a reduction in false positives by over 50% compared to the original protocol.
• A user-friendly PyMOL plugin was developed for easier application of the WaterDock protocols.

Conclusions:

• WaterDock2 represents a significant advancement in accurately predicting water molecules in protein binding sites.
• The reduction in false positives makes WaterDock2 particularly suitable for high-throughput screening in drug discovery.
• The integrated PyMOL plugin enhances accessibility and practical application of these computational tools.