Validation of AMBER/GAFF for Relative Free Energy Calculations

We computed relative binding free energies using GPU accelerated Thermodynamic Integration (GPU-TI) on a dataset originally assembled by Schrödinger, Inc.. Using their GPU enabled free energy code (FEP+) and the OPLS2.1 force field combined with REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol. In our study using GPU-TI of AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 mutations contained in this data set.

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Expanded Ensemble Methods Can Be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies

10.26434/chemrxiv.14665662.v1 ◽

2021 ◽

Author(s):

Si Zhang ◽

David Hahn ◽

Michael R. Shirts ◽

Vincent Voelz

Keyword(s):

Free Energy ◽

Force Field ◽

Ensemble Methods ◽

Free Energy Calculation ◽

Energy Calculation ◽

Free Energies ◽

Relative Binding ◽

Alchemical Free Energy ◽

Binding Free Energies ◽

Expanded Ensemble

Alchemical free energy methods have become indispensable in computational drug discovery for their ability to calculate highly accurate estimates of protein-ligand affinities. Expanded ensemble (EE) methods, which involve single simulations visiting all of the alchemical intermediates, have some key advantages for alchemical free energy calculation. However, there have been relatively few examples published in the literature of using expanded ensemble simulations for free energies of protein-ligand binding. In this paper, as a test of expanded ensemble methods, we computed relative binding free energies using the Open Force Field Initiative force field (codename “Parsley”) for twenty-four pairs of Tyk2 inhibitors derived from a congeneric series of 16 compounds. The EE predictions agree well with the experimental values (RMSE of 0.94 ± 0.13 kcal mol−1 and MUE of 0.75 ± 0.12 kcal mol−1). We find that while increasing the number of alchemical intermediates can improve the phase space overlap, faster convergence can be obtained with fewer intermediates, as long as the acceptance rates are sufficient. We find that convergence can be improved using more aggressive updating of the biases, and that estimates can be improved by performing multiple independent EE calculations. This work demonstrates that EE is a viable option for alchemical free energy calculation. We discuss the implications of these findings for rational drug design, as well as future directions for improvement.

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Expanded Ensemble Methods Can Be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies

10.26434/chemrxiv.14665662 ◽

2021 ◽

Author(s):

Si Zhang ◽

David Hahn ◽

Michael R. Shirts ◽

Vincent Voelz

Keyword(s):

Free Energy ◽

Force Field ◽

Ensemble Methods ◽

Free Energy Calculation ◽

Energy Calculation ◽

Free Energies ◽

Relative Binding ◽

Alchemical Free Energy ◽

Binding Free Energies ◽

Expanded Ensemble

Alchemical free energy methods have become indispensable in computational drug discovery for their ability to calculate highly accurate estimates of protein-ligand affinities. Expanded ensemble (EE) methods, which involve single simulations visiting all of the alchemical intermediates, have some key advantages for alchemical free energy calculation. However, there have been relatively few examples published in the literature of using expanded ensemble simulations for free energies of protein-ligand binding. In this paper, as a test of expanded ensemble methods, we computed relative binding free energies using the Open Force Field Initiative force field (codename “Parsley”) for twenty-four pairs of Tyk2 inhibitors derived from a congeneric series of 16 compounds. The EE predictions agree well with the experimental values (RMSE of 0.94 ± 0.13 kcal mol−1 and MUE of 0.75 ± 0.12 kcal mol−1). We find that while increasing the number of alchemical intermediates can improve the phase space overlap, faster convergence can be obtained with fewer intermediates, as long as the acceptance rates are sufficient. We find that convergence can be improved using more aggressive updating of the biases, and that estimates can be improved by performing multiple independent EE calculations. This work demonstrates that EE is a viable option for alchemical free energy calculation. We discuss the implications of these findings for rational drug design, as well as future directions for improvement.

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Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

Scientific Reports ◽

10.1038/s41598-020-80769-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Germano Heinzelmann ◽

Michael K. Gilson

Keyword(s):

Free Energy ◽

Early Stage ◽

Binding Free Energy ◽

Low Cost ◽

Free Energy Calculations ◽

Free Energies ◽

Energy Calculations ◽

Drug Candidates ◽

Binding Free Energy Calculations ◽

Binding Free Energies

AbstractAbsolute 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 automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged 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 positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

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Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

Chemical Science ◽

10.1039/d1sc03472c ◽

2021 ◽

Author(s):

Yuriy Khalak ◽

Gary Tresdern ◽

Matteo Aldeghi ◽

Hannah Magdalena Baumann ◽

David L. Mobley ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Drug Design ◽

Ligand Binding ◽

Binding Free Energy ◽

Free Energy Calculations ◽

Free Energies ◽

Energy Calculations ◽

Binding Free Energy Calculations ◽

Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

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Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

Interface Focus ◽

10.1098/rsfs.2019.0128 ◽

2020 ◽

Vol 10 (6) ◽

pp. 20190128 ◽

Cited By ~ 1

Author(s):

Shunzhou Wan ◽

Andrew Potterton ◽

Fouad S. Husseini ◽

David W. Wright ◽

Alexander Heifetz ◽

...

Keyword(s):

G Protein ◽

Binding Free Energy ◽

Absolute Error ◽

Free Energy Calculations ◽

Lead Optimization ◽

Receptor Protein ◽

Enhanced Sampling ◽

Free Energies ◽

G Protein Coupled ◽

Binding Free Energies

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A 1 and A 2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol −1 . Our methodology may be applied widely within the GPCR superfamily and to other small molecule–receptor protein systems.

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Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility

10.26434/chemrxiv-2021-nqp8r ◽

2021 ◽

Author(s):

Alexander Wade ◽

Agastya Bhati ◽

Shunzhou Wan ◽

Peter Coveney

Keyword(s):

Molecular Dynamics ◽

Free Energy ◽

Binding Free Energy ◽

Thermodynamic Integration ◽

Free Energy Perturbation ◽

Free Energies ◽

Ensemble Averaging ◽

Relative Binding ◽

Energy Perturbation ◽

Binding Free Energies

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat myriad diseases. In this work we examine the computation of alchemical relative binding free energies with an eye to assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2 and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2 and NAMD3. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between packages of 0.5 $kcal/mol$ The correlation between packages is very good with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficient between two packages being 0.91, 0.89 and 0.74 respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

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Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

10.26434/chemrxiv.9159872.v2 ◽

2019 ◽

Cited By ~ 1

Author(s):

David Slochower ◽

Niel Henriksen ◽

Lee-Ping Wang ◽

John Chodera ◽

David Mobley ◽

...

Keyword(s):

Ligand Binding ◽

Force Field ◽

Small Molecule ◽

Force Fields ◽

Free Energy Calculations ◽

Model Systems ◽

Conformational Space ◽

Free Energies ◽

Binding Thermodynamics ◽

Binding Free Energies

<div><div><div>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations. The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</div></div></div>

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Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

10.26434/chemrxiv.11874816.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ryther Anderson ◽

Diego Gómez-Gualdrón

Keyword(s):

Free Energy ◽

Force Field ◽

Large Scale ◽

Free Energy Calculations ◽

Free Energies ◽

Scale Free ◽

Computational Screening ◽

Synthetic Accessibility ◽

Isomorphic Series ◽

Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, ΔLMFFL, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below ΔLMFFL = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the ΔLMFFL threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

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Large-Scale Free Energy Calculations on a Computational MOF Database: Toward Synthetic Likelihood Predictions

10.26434/chemrxiv.11874816 ◽

2020 ◽

Author(s):

Ryther Anderson ◽

Diego Gómez-Gualdrón

Keyword(s):

Free Energy ◽

Force Field ◽

Large Scale ◽

Free Energy Calculations ◽

Free Energies ◽

Scale Free ◽

Computational Screening ◽

Synthetic Accessibility ◽

Isomorphic Series ◽

Metal Organic

Metal-organic frameworks (MOFs) have captivated the research community due to a modular crystal structure that is tailorable for many applications. However, with millions of possible MOFs to be considered, it is challenging to identify the ideal MOF for the application of choice. Although computational screening of MOF databases has provided a fast way to evaluate MOF properties, validation experiments on predicted “exceptional” MOFs are not common due to uncertainties on the synthetic likelihood of computationally constructed MOFs, hence hindering material discovery. Aiming to leverage the perspective provided by large datasets, here we created and screened a topologically diverse database of 8,500 MOFs to interrogate whether thermodynamic stability metrics such as free energy could be used to generally predict the synthetic likelihood of computationally constructed MOFs. To this end, we first evaluated the suitability of two methods and three force fields to calculate free energies in MOFs at large scale, settling on the Frenkel-Ladd path thermodynamic integration method and the UFF4MOF force field. Upon defining a relative free energy, ΔLMFFL, that corrects for some force field artifacts specific to MOF nodes, we found that previously synthesized MOFs tended to cluster in a region below ΔLMFFL = 4.4 kJ/mol per atom, suggesting a general first filter to discriminate between synthetically likely and unlikely MOFs. However, a second filter is needed when several MOF isomorphs are below the ΔLMFFL threshold. In 84% of the cases, the synthetically accessible MOF within an isomorphic series presented the lowest predicted free energy. The present; work suggests that crystal free energies could be key to understanding synthetic likelihood for MOFs in computational databases (and MOFs in general), and that the thermodynamics stability of the fully assembled MOF often determines synthetic accessibility.

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