BFEE: A User-Friendly Graphical Interface Facilitating Absolute Binding Free-Energy Calculations

Fu, Haohao; Gumbart, James C.; Chen, Haochuan; Shao, Xueguang; Cai, Wensheng; Chipot, Christophe

doi:10.1021/acs.jcim.7b00695

Cited by 65 publications

(94 citation statements)

References 24 publications

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“…[37][38][39] The challenge in the RBFE calculations setup is to automatically recognize the structural differences between the ligands and prepare a sensible hybrid topology for MD simulations. Several programs that help with this [40][41][42][43] and other steps in the process 44,45 have been reported. Work from the de Groot lab has led to the development of pmx, 46,47 a tool to prepare inputs for alchemical free energy calculations 48 in GROMACS.…”

Section: Introductionmentioning

confidence: 99%

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

et al. 2020

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“…One of the major challenges in modern computational biochemistry is the accurate and reliable calculation of affinities between proteins and associated binding partners . A very prominent method to calculate free‐energy changes upon a ligand binding to a host protein or DNA is to simulate a nonphysical path where the ligand is alchemically perturbed in the protein and free in solution .…”

Section: Introductionmentioning

confidence: 99%

Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

et al. 2020

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Alchemically derived free energies are artifacted when the perturbed moiety has a nonzero net charge. The source of the artifacts lies in the effective treatment of the electrostatic interactions within and between the perturbed atoms and remaining (partial) charges in the simulated system. To treat the electrostatic interactions effectively, lattice‐summation (LS) methods or cutoff schemes in combination with a reaction‐field contribution are usually employed. Both methods render the charging component of the calculated free energies sensitive to essential parameters of the system like the cutoff radius or the box side lengths. Here, we discuss the results of three previously published studies of ligand binding. These studies presented estimates of binding free energies that were artifacted due to the charged nature of the ligands. We show that the size of the artifacts can be efficiently calculated and raw simulation data can be corrected. We compare the corrected results with experimental estimates and nonartifacted estimates from path‐sampling methods. Although the employed correction scheme involves computationally demanding continuum‐electrostatics calculations, we show that the correction estimate can be deduced from a small sample of configurations rather than from the entire ensemble. This observation makes the calculations of correction terms feasible for complex biological systems. To show the general applicability of the proposed procedure, we also present results where the correction scheme was used to correct independent free energies obtained from simulations employing a cutoff scheme or LS electrostatics. In this work, we give practical guidelines on how to apply the appropriate corrections easily.

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“…Important prior contributions toward automation of ABFE calculations include the CHARMM-GUI server 33,34 and the binding free energy estimator (BFEE). 35,36 The former is a web-based interface that helps create input files for the various stages listed above. The latter is a tcl plug-in for VMD, 37 with a graphical interface that creates a complete ABFE workflow starting from an initial prepared and equilibrated protein-ligand complex.…”

Section: Introductionmentioning

confidence: 99%

Automated docking refinement and virtual compound screening with absolute binding free energy calculations

Heinzelmann¹,

Gilson

2020

Preprint

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Absolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to fully automate the calculation of binding free energies for a protein with a series of ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost allows this procedure to be applied in a relatively high-throughput mode, and thus enables new applications in early-stage drug discovery.Protein-ligand binding free energy calculations based on atomistic molecular simulations promise to play a growing role in drug discovery, as they provide estimates of the binding affinities of compounds proposed as drug candidates for a protein target, and thus may reduce the time and cost required for trial-and-error experimentation. 1,2 Thus, in settings where the calculations are sufficiently fast and accurate, 3 one may anticipate significant savings of time and cost in early stages of drug discovery. [4][5][6][7] It is informative to divide this broad class of methods into two subtypes: relative binding free energy (RBFE) calculations; [8][9][10][11][12][13][14] and absolute binding free energy (ABFE) calculations. [15][16][17][18][19][20][21][22][23] The former (RBFE) estimate the difference in binding free energy between two compounds by computing the change in free energy associated with a non-physical transformation of one compound to the other, in the binding site and in bulk solvent. 8,24 Because it is easiest to carry out such alchemical transformations between compounds that are similar to each other and that adopt similar bound poses, RBFE calculations are often regarded as particularly suitable for the leadoptimization stage of drug discovery, where small chemical modifications of the initial lead compound must be selected. Interestingly, though, a recent study reports greater impact on the earlier hit-to-lead stage. 3

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BFEE: A User-Friendly Graphical Interface Facilitating Absolute Binding Free-Energy Calculations

Cited by 65 publications

References 24 publications

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

Automated docking refinement and virtual compound screening with absolute binding free energy calculations

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