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Updated: Jul 11, 2025

Assaying Protein Kinase Activity with Radiolabeled ATP
Published on: May 26, 2017
Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases
Candide Champion1, René Gall1, Benjamin Ries1
1Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland.
Replica-exchange enveloping distribution sampling (RE-EDS) allows calculating multiple ligand binding free-energy differences in one simulation. This alchemical free-energy method significantly reduces computational cost in drug design.
Area of Science:
- Computational chemistry
- Molecular dynamics simulations
- Drug discovery
Background:
- Alchemical free-energy methods aid in optimizing small organic molecules for improved protein binding affinity.
- Pairwise free-energy calculations become computationally prohibitive as the number of molecules to assess increases.
Purpose of the Study:
- To introduce and validate replica-exchange enveloping distribution sampling (RE-EDS) for calculating multi-ligand alchemical free-energy differences.
- To demonstrate the general applicability of RE-EDS in prospective drug design campaigns.
Main Methods:
- Developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method.
- Applied RE-EDS to a set of four kinases and 42 corresponding inhibitors.
- Performed single molecular dynamics (MD) simulations to calculate free-energy differences between multiple ligands simultaneously.
Main Results:
- RE-EDS successfully modeled up to 13 ligands concurrently for the studied kinase targets.
- High sampling efficiency was achieved with the RE-EDS method.
- A substantial reduction in computational cost was observed compared to traditional pairwise methods.
Conclusions:
- RE-EDS offers a computationally efficient approach for alchemical free-energy calculations involving multiple ligands.
- The method shows promise for accelerating lead optimization in drug design campaigns.
- RE-EDS provides a scalable solution to the combinatorial challenge of assessing numerous potential drug candidates.
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