Absolute Binding Free Energies between T4 Lysozyme and 141 Small Molecules: Calculations Based on Multiple Rigid Receptor Configurations

Xie, Bing; Nguyen, Trung Hai; Minh, David D. L.

doi:10.1021/acs.jctc.6b01183

Cited by 37 publications

(99 citation statements)

References 75 publications

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“…Docking algorithms are designed to estimate binding affinities between proteins and ligands using phenomenological scoring functions based on (nearly) rigid representations of the molecules. Although comparative studies (including SAMPL challenges) consistently illustrate that binding free energies obtained from docking simulations poorly correlate with experiment [8–10,70,71], if used properly, molecular docking approaches offer useful intuitions such as plausible configurations of a protein-ligand complex in a fast and computationally inexpensive way.…”

Section: Discussionmentioning

confidence: 99%

Prediction of CB[8] host–guest binding free energies in SAMPL6 using the double-decoupling method

Han

Hudson

Jones

et al. 2018

J Comput Aided Mol Des

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This study reports the results of binding free energy calculations for CB[8] host-guest systems in the SAMPL6 blind challenge (receipt ID 3z83m). Force-field parameters were developed specific for each of host and guest molecules to improve configurational sampling. We used quantum mechanical (QM) implicit solvent calculations and QM force matching to determine non-bonded (partial atomic charges) and bonded terms, respectively. Free energy calculations were carried out using the double-decoupling method (DDM) combined with Hamiltonian replica exchange method (HREM) and Bennett acceptance ratio (BAR) method. The root mean square error (RMSE) of the predicted values using DDM with respect to the experimental results was 4.32 kcal/mol. The coefficient of determination (R) and Kendall rank coefficient (τ) were 0.49 and 0.52, respectively, highest of all submissions. In addition, these were compared to the results obtained by umbrella sampling (US) and weighted histogram analysis method (WHAM). Overall, DDM achieved a higher prediction accuracy than the US method. Results are discussed in terms of parameterization and free energy simulations.

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Section: Discussionmentioning

confidence: 99%

Prediction of CB[8] host–guest binding free energies in SAMPL6 using the double-decoupling method

Han

Hudson

Jones

et al. 2018

J Comput Aided Mol Des

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show abstract

“…The inverse transformation power m = 4 provides a good balance between accuracy and computational expense. (This inverse power was used in my group's recent BPMF‐based protein‐ligand binding free energy calculations …”

Section: Discussionmentioning

confidence: 99%

Power transformations improve interpolation of grids for molecular mechanics interaction energies

Minh

2018

J Comput Chem

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A common strategy for speeding up molecular docking calculations is to precompute nonbonded interaction energies between a receptor molecule and a set of three-dimensional grids. The grids are then interpolated to compute energies for ligand atoms in many different binding poses. Here, I evaluate a smoothing strategy of taking a power transformation of grid point energies and inverse transformation of the result from trilinear interpolation. For molecular docking poses from 85 protein-ligand complexes, this smoothing procedure leads to significant accuracy improvements, including an approximately twofold reduction in the root mean square error at a grid spacing of 0.4 Å and retaining the ability to rank docking poses even at a grid spacing of 0.7 Å. © 2018 Wiley Periodicals, Inc.

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“…We have successfully applied AlGDock to estimating binding free energies between a small hydrophobic cavity in the T4 lysozyme L99A mutant and 141 fragment-like organic molecules [19]. We have also computed BPMFs for the Astex Diverse Set, a curated subset of 85 protein-ligand complexes in the Protein Data Bank with pharmaceutical or agricultural relevance [20].…”

Section: Introductionmentioning

confidence: 99%

Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3

Xie

Minh

2018

J Comput Aided Mol Des

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We participated in Subchallenges 1 and 2 of the Drug Design Data Resource (D3R) Grand Challenge 3. To prepare our submissions, we performed molecular docking with UCSF DOCK 6 and binding potential of mean force (BPMF) calculations-free energy calculations between flexible ligands and rigid receptors-using our open-source software package Alchemical Grid Dock (AlGDock). For each system, submissions were based on the minimum BPMF calculated for a selected set of crystal structures. In Subchallenge 1, our workflow performed poorly. Possible reasons for the poor performance include the neglect of cooperative ligands and limited sampling of ligand binding poses. In Subchallenge 2, our workflow led to some of most highly correlated submissions (Pearson R = 0.5) for vascular endothelial growth factor receptor 2. However, our results were poorly correlated for Janus Kinase 2 and Mitogen-activated protein kinase 14. Affinity prediction could potentially be improved by systematic selection of more diverse receptor configurations.

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Absolute Binding Free Energies between T4 Lysozyme and 141 Small Molecules: Calculations Based on Multiple Rigid Receptor Configurations

Cited by 37 publications

References 75 publications

Prediction of CB[8] host–guest binding free energies in SAMPL6 using the double-decoupling method

Prediction of CB[8] host–guest binding free energies in SAMPL6 using the double-decoupling method

Power transformations improve interpolation of grids for molecular mechanics interaction energies

Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3

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