Alchemical Grid Dock (AlGDock): Binding Free Energy Calculations between Flexible Ligands and Rigid Receptors

Minh, David D. L.

doi:10.1002/jcc.26036

Cited by 18 publications

(13 citation statements)

References 52 publications

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“…51 In brief, AlGDock uses Hamiltonian replica exchange for two processes: cooling and docking. In both processes, only the ligand molecule is flexible.…”

Section: Theory and Methodsmentioning

confidence: 99%

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

Xie

Nguyen

Minh

2017

J. Chem. Theory Comput.

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We demonstrate the feasibility of estimating protein-ligand binding free energies using multiple rigid receptor configurations. Based on T4 lysozyme snapshots extracted from six alchemical binding free energy calculations with a flexible receptor, binding free energies were estimated for a total of 141 ligands. For 24 ligands, the calculations reproduced flexible-receptor estimates with a correlation coefficient of 0.90 and a root mean square error of 1.59 kcal/mol. The accuracy of calculations based on Poisson-Boltzmann/Surface Area implicit solvent was comparable to previously reported free energy calculations.

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“…51 In brief, AlGDock uses Hamiltonian replica exchange for two processes: cooling and docking. In both processes, only the ligand molecule is flexible.…”

Section: Theory and Methodsmentioning

confidence: 99%

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

Xie

Nguyen

Minh

2017

J. Chem. Theory Comput.

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“…Among the methodologies that have been developed to carry out this task, physics-based methods employing classical force fields are starting to be routinely used in drug development projects and demonstrate success in real lead optimization scenarios [2][3][4][5]. These technologies are also often employed to obtain mechanistic insights into the physics of binding such as the discovery of binding poses [6] and pathways [7], or attempts at providing intuitive guidance on how to improve ligand binding potency [8]. However, the applicability domain of these models is currently limited to a narrow portion of the accessible chemical space for small molecules, and well-behaved protein-ligand systems that do not undergo significant conformational changes or solvent displacement on timescales larger than a few tens of nanoseconds [9,10].…”

Section: Introductionmentioning

confidence: 99%

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

Rizzi

Jensen

Slochower

et al. 2020

J Comput Aided Mol Des

125

189

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Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange-while displaying very small variance-can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

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“…BPMFs were estimated using the version of AlGDock posted on GitHub [14] on Sep 3, 2017. The key algorithms behind AlGDock are described in a 2015 technical report [13].…”

Section: Methodsmentioning

confidence: 99%

“…We have developed an open-source computer program, Alchemical Grid Dock (AlGDock) [13,14], to compute protein-ligand BPMFs. As in many other free energy calculations, AlGDock uses Hamiltonian replica exchange [15–17].…”

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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Alchemical Grid Dock (AlGDock): Binding Free Energy Calculations between Flexible Ligands and Rigid Receptors

Abstract: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Cited by 18 publications

References 52 publications

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

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

The SAMPL6 SAMPLing challenge: assessing the reliability and efficiency of binding free energy calculations

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

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