Optimizing Absolute Binding Free Energy Calculations for Production Usage.

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Protein-ligand binding affinity prediction is crucial for small molecule drug discovery.
• Alchemical absolute binding free-energy (ABFE) calculations are accurate but can suffer from instability and poor convergence in large-scale projects.

Purpose of the Study:

• To optimize the ABFE protocol for enhanced stability, convergence, and precision in protein-ligand binding affinity prediction.
• To address limitations of current ABFE methods in large-scale drug discovery pipelines.

Main Methods:

• Developed a novel algorithm for protein-ligand pose restraint selection, incorporating hydrogen bond data to prevent numerical instabilities and improve convergence.
• Optimized the annihilation protocol to minimize free energy error.
• Reordered the scaling of interactions (electrostatics, Lennard-Jones, restraints, intramolecular torsions) to systematically enhance precision.

Main Results:

• The optimized ABFE protocol demonstrated significantly lower variances in free energy results across four benchmark systems (TYK2, P38, JNK1, CDK2).
• Achieved improvements of up to 0.23 kcal/mol in root-mean-square error compared to the original protocol.
• The modifications led to more stable and reliable simulations.

Conclusions:

• The implemented optimizations substantially improve the accuracy, precision, and reliability of alchemical ABFE calculations for protein-ligand binding affinity prediction.
• These enhanced protocols offer a more robust tool for computational drug discovery, enabling more efficient screening of potential drug candidates.