An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual...

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Molecular mechanics generalized Born surface area (MM-GBSA) methods are widely used for predicting binding affinities.
• Standard MM-GBSA methods can be limited in accuracy for certain systems, such as protein-protein interactions.

Purpose of the Study:

• To introduce and validate Nwat-MMGBSA, a novel MM-GBSA variant incorporating explicit water molecules.
• To optimize a computational protocol for Nwat-MMGBSA to enhance efficiency and accuracy.
• To assess the performance of Nwat-MMGBSA in structure-based virtual screening.

Main Methods:

• Nwat-MMGBSA was developed by including explicit water molecules closest to the ligand in molecular dynamics (MD) trajectories.
• Protocol optimization was performed using penicillopepsin, HIV1-protease, and BCL-XL as test cases.
• Nwat-MMGBSA calculations were validated on different computational environments (HPC, GPU workstations) and trajectory lengths (1-4 ns).
• A fully automated virtual screening workflow incorporating Nwat-MMGBSA rescoring was developed and tested.

Main Results:

• Nwat-MMGBSA showed improved correlations between calculated and experimental binding energies compared to standard MM-GBSA.
• No significant statistical differences were observed across different computational setups or trajectory lengths.
• Retrospective virtual screening using Nwat-MMGBSA rescoring resulted in a 20-30% increase in ROC AUCs compared to docking or standard MM-GBSA.

Conclusions:

• Nwat-MMGBSA offers a more accurate and robust approach for binding energy calculations.
• The developed automated workflow significantly enhances the effectiveness of structure-based virtual screening.
• Nwat-MMGBSA represents a valuable advancement for computational drug discovery and molecular interaction studies.