Absolute Binding Free Energy Calculations between the SARS-CoV-2 Main Protease and 130 Drug Leads Using Implicit...

Area of Science:

• Computational Chemistry
• Drug Discovery
• Structural Biology

Background:

• Absolute binding free energy (ΔG) calculations are crucial for ranking drug candidates.
• Flexible protein systems pose challenges for accurate ΔG calculations due to sampling difficulties.
• SARS-CoV-2 main protease (MPro) is a key target for antiviral drug development.

Purpose of the Study:

• To develop a computationally efficient method for converged ΔG calculations in flexible receptor systems.
• To assess the accuracy of this method for ranking drug leads against SARS-CoV-2 MPro.
• To provide a valuable tool for early-stage drug discovery.

Main Methods:

• Utilized extensive molecular dynamics simulations of apo SARS-CoV-2 MPro from Folding@Home.
• Constructed a Markov state model (MSM) to determine equilibrium probabilities.
• Computed binding potential of mean force (BPMF) using representative snapshots and implicit ligand theory (ILT).

Main Results:

• Achieved a Pearson R of 0.55 and a mean-adjusted RMSE of 1.6 kcal/mol when comparing calculated ΔG to experimental values.
• The method's accuracy and computational cost were intermediate between docking and fully flexible receptor approaches.
• In 88% of cases, the native binding pose's ΔG was within 1kT of the top-ranked pose.

Conclusions:

• The developed MSM-based approach enables accurate and efficient ΔG calculations for flexible systems.
• This method offers a practical balance between computational cost and predictive accuracy for drug discovery.
• The findings support the use of this technique for ranking drug leads against MPro and other flexible targets.