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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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An ABSINTH-Based Protocol for Predicting Binding Affinities between Proteins and Small Molecules.

Jean-Rémy Marchand¹, Tim Knehans¹, Amedeo Caflisch¹

¹Department of Biochemistry, University of Zürich, CH 8057 Zürich, Switzerland.

Journal of Chemical Information and Modeling

|September 8, 2020

Summary

This study enhances computational drug discovery by improving binding free energy predictions for receptor-ligand systems. The new method accurately ranks known binders and enriches true binders in virtual screening workflows.

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Published on: December 1, 2020

Area of Science:

Computational chemistry
Drug discovery
Biophysics

Background:

Accurate prediction of binding free energies is crucial for computational drug discovery.
Existing methods face challenges in scoring functions for virtual screening.

Purpose of the Study:

To extend the ABSINTH implicit solvent model and force field for small organic molecules and protein interactions.
To develop an automated pipeline for predicting binding free energies in receptor-ligand systems.

Main Methods:

Partitioning molecules into substructures with known free energies of solvation.
Combining with the CHARMM general force field for a novel scoring function.
Incorporating explicit ions and integrating over multiple protonation states.

Main Results:

The developed method rivals state-of-the-art techniques in ranking known binders.
The protocol successfully enriches true binders within decoy sets.
Modifications to the protocol and ABSINTH model demonstrated significant value.

Conclusions:

The proposed high-throughput implicit method shows promise for virtual screening.
Further discussion on the limitations of such high-throughput implicit methods is warranted.