Fragment-based computation of binding free energies by systematic sampling

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Accurate prediction of protein-ligand binding free energies is crucial for drug discovery.
• Existing methods often suffer from limited conformational sampling and dependence on initial poses.

Purpose of the Study:

• To develop a novel fragment-based computational method for calculating protein-ligand binding free energies.
• To enable rigorous and comprehensive sampling of molecular interactions and conformations.

Main Methods:

• Systematic sampling of fragment-protein interactions in translational and rotational space.
• De novo assembly of fragments into potential drug molecules.
• Computation of binding free energies using statistical mechanics, including direct entropy loss calculation.

Main Results:

• The method demonstrates independence from initial binding poses, allowing for a fuller exploration of conformational space.
• Accurate prediction of binding free energies with a standard error of approximately 1 kcal/mol was achieved for T4 lysozyme and p38 MAP kinase ligands.
• The approach efficiently evaluates numerous molecular poses with reduced computational cost.

Conclusions:

• This fragment-based method offers a rigorous and efficient approach to predicting protein-ligand binding free energies.
• The technique overcomes key limitations of prior free energy methods, particularly the "conformational focusing" problem.
• The developed methodology holds significant promise for advancing rational drug design and molecular modeling.