Efficient Protein-Ligand Binding Free Energy Estimation with Coarse-Grained Funnel Metadynamics

Area of Science:

• Computational chemistry
• Biophysics
• Drug discovery

Background:

• Accurate prediction of protein-ligand binding free energy remains a challenge.
• All-atom molecular dynamics (AA-MD) is accurate but computationally expensive.
• Docking methods are fast but lack accuracy.

Purpose of the Study:

• To develop a computationally efficient method for accurate binding free energy prediction.
• To bridge the gap between AA-MD accuracy and docking throughput.
• To validate coarse-grained funnel metadynamics (CG-FMD) for binding free energy calculations.

Main Methods:

• Coarse-grained funnel metadynamics (CG-FMD) using the Martini 3 force field.
• Modeling colchicine binding to two protein targets at both all-atom (AA) and coarse-grained (CG) resolutions.
• Extensive simulations totaling over 7 ms to assess prediction robustness.

Main Results:

• CG-FMD predictions for ΔGbind were comparable to experimental values.
• The method achieved this accuracy with a fraction of the computational cost of AA-MD.
• Extensive sampling reduced statistical uncertainty, compensating for the simplified CG representation.

Conclusions:

• CG-FMD provides a robust and computationally efficient approach for predicting protein-ligand binding free energies.
• This method holds promise for accelerating drug discovery by enabling high-throughput screening.
• Further studies should expand the range of ligands and targets investigated.