Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure
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Date
2022-02
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Journal Article
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Abstract
The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a "state graph", in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.
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published
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Journal / series
Volume
36 (2)
Pages / Article No.
117 - 130
Publisher
Springer
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Date collected
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Subject
Molecular dynamics; Free energy calculation; Protein-ligand binding; Replica exchange; Enveloping distribution sampling
Organisational unit
09458 - Riniker, Sereina Z. / Riniker, Sereina Z.
Notes
Funding
178762 - Passive Membrane-Permeability Prediction for Peptides and Peptidomimetics Using Computational Methods (SNF)
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