Rapid, Accurate, Precise, and Reliable Relative Free Energy Prediction Using Ensemble Based Thermodynamic Integration

Area of Science:

• Computational chemistry
• Molecular modeling

Background:

• Accurate prediction of ligand-protein binding affinities is vital for drug discovery and personalized medicine.
• Existing methods, like ensemble-based molecular dynamics with the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method, offer reliability but can be time-consuming.
• Balancing prediction accuracy, precision, reliability, and computational speed is a key challenge.

Purpose of the Study:

• To develop and validate a computational methodology that substantially improves the speed, accuracy, precision, and reliability of calculated relative binding free energies.
• To present a systematic approach for estimating prediction uncertainties.

Main Methods:

• The study employs a thermodynamic integration (TI) based methodology.
• This approach is designed as an equivalent to ensemble-based molecular dynamics but aims for enhanced performance.
• The method's performance is evaluated across a diverse set of protein targets and ligands.

Main Results:

• The developed TI method demonstrates very good agreement with experimental data, with 90% of calculations accurate to within 1 kcal/mol.
• The methodology achieves reproducible results by construction.
• Statistical uncertainties in the predictions are consistently low, typically 0.5 kcal/mol or less.

Conclusions:

• The thermodynamic integration method offers a significant advancement in predicting relative binding free energies.
• This approach meets the critical requirements of speed, accuracy, precision, and reliability for drug discovery applications.
• The systematic estimation of prediction uncertainty enhances the trustworthiness of the computational results.