Combining QM/MM Calculations with Classical Mining Minima to Predict Protein-Ligand Binding Free Energy

Area of Science:

• Computational Chemistry
• Molecular Modeling
• Drug Discovery

Background:

• Accurate prediction of binding free energy (BFE) is crucial for efficient drug discovery.
• Existing methods for BFE prediction have limitations in accuracy or computational cost.
• Quantum mechanical/molecular mechanical (QM/MM) approaches offer potential for improved accuracy.

Purpose of the Study:

• To develop and validate a novel QM/MM-based protocol for enhanced binding free energy prediction.
• To assess the performance of the new protocol against established BFE calculation methods.
• To evaluate the computational efficiency of the developed protocol for practical applications in drug discovery.

Main Methods:

• Developed the Qcharge-VM2 protocol integrating QM/MM calculations with the VeraChem mining minima engine.
• Replaced standard force field atomic charges with QM-recalculated charges at proposed ligand poses.
• Tested the protocol on seven targets and 147 diverse ligands, comparing results with classical mining minima and popular BFE methods.

Main Results:

• The Qcharge-VM2 protocol achieved a high overall Pearson correlation of 0.86, outperforming all compared methods.
• Demonstrated significantly better performance than implicit solvent methods (MM-GBSA, MM-PBSA).
• Showed competitive accuracy (RMSE: 1.75 kcal/mol, MUE: 1.39 kcal/mol) compared to explicit solvent methods (FEP+), but with substantially lower computational demand.

Conclusions:

• The Qcharge-VM2 protocol offers an effective and computationally efficient approach for binding free energy prediction.
• This method presents a valuable tool for accelerating drug discovery campaigns by improving the accuracy-cost balance.
• The integration of QM/MM calculations provides a robust strategy for refining molecular mechanics force fields in BFE studies.