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Tuning Potential Functions to Host-Guest Binding Data.

Jeffry Setiadi1, Simon Boothroyd2,3, David R Slochower4

  • 1Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, 9255 Pharmacy Lane, La Jolla, California 92093, United States.

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|December 26, 2023
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Summary
This summary is machine-generated.

This study introduces a new method using host-guest systems to train force fields for predicting protein-ligand binding affinities more accurately. The optimized parameters show improved binding predictions and faster calculations, aiding drug discovery.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Molecular modeling

Background:

  • Physics-based methods are crucial for predicting protein-ligand binding affinities in drug discovery.
  • The accuracy of these methods relies heavily on the quality of potential functions used for simulations.
  • Experimental binding affinities have not been extensively used to train these potential functions.

Purpose of the Study:

  • To extend the Open Force Field Evaluator framework for calculating host-guest binding free energies.
  • To curate experimental host-guest binding data for training potential functions.
  • To optimize generalized Born (GB) cavity radii for the OBC2 implicit solvent model using host-guest data.

Main Methods:

  • Systematic calculation of host-guest binding free energies and their gradients.
  • Curation of 126 host-guest complexes with experimental binding free energies.
  • Optimization of OBC2 GB model cavity radii against 36 host-guest systems.

Main Results:

  • A dramatic improvement in accuracy for both training and test sets of host-guest systems.
  • Demonstrated transferability of optimized radii from host-guest to protein-ligand systems.
  • Identified a trade-off between accurate binding affinities and hydration free energies with the optimized radii.

Conclusions:

  • Host-guest systems can effectively train transferable potential functions for protein-ligand binding affinity prediction.
  • The developed infrastructure enables new applications in computational drug discovery.
  • Implicit solvent models offer significant speedups for binding free-energy calculations, suggesting utility in virtual screening.