Parameterization of an effective potential for protein-ligand binding from host-guest affinity data

Area of Science:

• Biomolecular modeling and computational chemistry.
• Drug discovery and development.
• Structural biology and biophysics.

Background:

• Force field accuracy remains a significant challenge in biomolecular modeling.
• Organic host-guest complexes provide valuable experimental data for validating computational models.
• Cyclodextrin (CD) inclusion complexes are well-suited models for molecular recognition studies.

Purpose of the Study:

• To develop and validate improved implicit solvent parameters for biomolecular force fields.
• To utilize experimental binding affinity data for optimizing energy parameters.
• To enhance the accuracy of protein-ligand binding free energy calculations.

Main Methods:

• Employed the binding energy distribution analysis method (BEDAM) with experimental binding affinity data.
• Validated new solvation parameters using Grid Inhomogeneous Solvation Theory (GIST).
• Applied the optimized parameters to protein-ligand binding in HIV-1 drug targets.

Main Results:

• Successfully developed new implicit solvent parameters using CD host-guest binding data.
• Demonstrated improved agreement between calculated and experimental binding affinities for HIV-1 drug targets.
• Validated the robustness and utility of the developed parameters for force field optimization.

Conclusions:

• High-quality experimental binding affinity data is a reliable resource for tuning biomolecular force fields.
• Physics-based binding free energy models and benchmark datasets are essential for force field evaluation.
• The developed parameters offer a promising approach for more accurate protein-ligand interaction predictions.