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.
Highlights
Rigorous free-energy calculations using molecular dynamics (MD) simulations have become an important tool to estimate binding free energies of novel compounds for lead optimization in drug discovery [1,2,3]
Gbjiind with the difference of the Gbi ind obtained from experiment [6, 7], Gbjiind = Gpjirotein − Gwji ater = Gbj ind − Gbi ind Conventional free-energy methods such as thermodynamic integration (TI) [8] and free-energy perturbation (FEP) [9] introduce a coupling parameter to define a pathway from end state i ( = 0 ) to end state j ( = 1 )
The parameter exploration consists of three substeps: (i) determining the lower bound for the s-distribution, (ii) obtaining optimized coordinates within the enveloping distribution sampling (EDS) set-up for each end state, and (iii) estimation of an initial set of energy offsets
Summary
Rigorous free-energy calculations using molecular dynamics (MD) simulations have become an important tool to estimate binding free energies of novel compounds for lead optimization in drug discovery [1,2,3]. To estimate the binding free energy of five compounds, a “state graph” can Gbjiind with the difference of the Gbi ind obtained from experiment [6, 7], Gbjiind = Gpjirotein − Gwji ater = Gbj ind − Gbi ind (1). Conventional free-energy methods such as thermodynamic integration (TI) [8] and free-energy perturbation (FEP) [9] introduce a coupling parameter to define a pathway from end state i ( = 0 ) to end state j ( = 1 ). With (RE-)EDS, all end states in a given environment can be considered simultaneously in a single simulation of a reference state (green circles) simulations at discrete intermediate -points are performed to obtain converged free-energy differences
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