Implicit ligand theory for relative binding free energies.

Area of Science:

• Computational chemistry
• Molecular modeling
• Biophysics

Background:

• Implicit ligand theory calculates noncovalent binding free energies.
• It relies on averaging the binding potential of mean force (BPMF) over receptor configurations.
• Previously, configurations were drawn from or reweighted to the apo ensemble.

Purpose of the Study:

• To investigate using holo ensembles for BPMF averaging in implicit ligand theory.
• To develop a new statistical estimator for binding free energy calculations.
• To compare the performance of holo versus apo ensembles.

Main Methods:

• Averaging BPMF over a precomputed holo ensemble of receptor configurations.
• Utilizing receptor snapshots from alchemical simulations.
• Comparing the new statistical estimator with the original formalism.

Main Results:

• Averaging BPMF over a holo ensemble yields binding free energies relative to a reference ligand.
• The new statistical estimator outperforms the original method when using receptor snapshots from a single-ligand alchemical simulation.
• This demonstrates improved accuracy and efficiency.

Conclusions:

• Implicit ligand theory can be effectively applied using holo ensembles.
• The new holo ensemble averaging method provides a more accurate and robust statistical estimator for binding free energies.
• This advancement has implications for drug discovery and molecular design.