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On Calculating Free Energy Differences Using Ensembles of Transition Paths

The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of as...

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Autores principales: Hall, Robert, Dixon, Tom, Dickson, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291376/
https://www.ncbi.nlm.nih.gov/pubmed/32582764
http://dx.doi.org/10.3389/fmolb.2020.00106
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author Hall, Robert
Dixon, Tom
Dickson, Alex
author_facet Hall, Robert
Dixon, Tom
Dickson, Alex
author_sort Hall, Robert
collection PubMed
description The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (k(on)) and dissociation (k(off))—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate than the binding affinity as they depend on details of the transition path ensemble. Assessing these rate constants can be difficult, due to uncertainty in the definition of the bound and unbound states, large error bars and the lack of experimental data. As an additional consistency check, rate constants from simulation can be used to calculate free energies (using the log of their ratio) which can then be compared to free energies obtained experimentally or using alchemical free energy perturbation. However, in this calculation it is not straightforward to account for common, practical details such as the finite simulation volume or the particular definition of the “bound” and “unbound” states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using full reactive trajectories. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol. Although these terms were derived with weighted ensemble simulations in mind, some of the correction terms are generally applicable to free energies calculated using physical pathways via methods such as Markov state modeling, metadynamics, milestoning, or umbrella sampling.
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spelling pubmed-72913762020-06-23 On Calculating Free Energy Differences Using Ensembles of Transition Paths Hall, Robert Dixon, Tom Dickson, Alex Front Mol Biosci Molecular Biosciences The free energy of a process is the fundamental quantity that determines its spontaneity or propensity at a given temperature. In particular, the binding free energy of a drug candidate to its biomolecular target is used as an objective quantity in drug design. Recently, binding kinetics—rates of association (k(on)) and dissociation (k(off))—have also demonstrated utility for their ability to predict efficacy and in some cases have been shown to be more predictive than the binding free energy alone. Some methods exist to calculate binding kinetics from molecular simulations, although these are typically more difficult to calculate than the binding affinity as they depend on details of the transition path ensemble. Assessing these rate constants can be difficult, due to uncertainty in the definition of the bound and unbound states, large error bars and the lack of experimental data. As an additional consistency check, rate constants from simulation can be used to calculate free energies (using the log of their ratio) which can then be compared to free energies obtained experimentally or using alchemical free energy perturbation. However, in this calculation it is not straightforward to account for common, practical details such as the finite simulation volume or the particular definition of the “bound” and “unbound” states. Here we derive a set of correction terms that can be applied to calculations of binding free energies using full reactive trajectories. We apply these correction terms to revisit the calculation of binding free energies from rate constants for a host-guest system that was part of a blind prediction challenge, where significant deviations were observed between free energies calculated with rate ratios and those calculated from alchemical perturbation. The correction terms combine to significantly decrease the error with respect to computational benchmarks, from 3.4 to 0.76 kcal/mol. Although these terms were derived with weighted ensemble simulations in mind, some of the correction terms are generally applicable to free energies calculated using physical pathways via methods such as Markov state modeling, metadynamics, milestoning, or umbrella sampling. Frontiers Media S.A. 2020-06-05 /pmc/articles/PMC7291376/ /pubmed/32582764 http://dx.doi.org/10.3389/fmolb.2020.00106 Text en Copyright © 2020 Hall, Dixon and Dickson. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Hall, Robert
Dixon, Tom
Dickson, Alex
On Calculating Free Energy Differences Using Ensembles of Transition Paths
title On Calculating Free Energy Differences Using Ensembles of Transition Paths
title_full On Calculating Free Energy Differences Using Ensembles of Transition Paths
title_fullStr On Calculating Free Energy Differences Using Ensembles of Transition Paths
title_full_unstemmed On Calculating Free Energy Differences Using Ensembles of Transition Paths
title_short On Calculating Free Energy Differences Using Ensembles of Transition Paths
title_sort on calculating free energy differences using ensembles of transition paths
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291376/
https://www.ncbi.nlm.nih.gov/pubmed/32582764
http://dx.doi.org/10.3389/fmolb.2020.00106
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