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Non-Markovian modeling of protein folding

We extract the folding free energy landscape and the time-dependent friction function, the two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular dynamics (MD) simulations of the [Formula: see text]-helix forming polypeptide [Formula: see text] for a one-dimensiona...

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Autores principales: Ayaz, Cihan, Tepper, Lucas, Brünig, Florian N., Kappler, Julian, Daldrop, Jan O., Netz, Roland R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346879/
https://www.ncbi.nlm.nih.gov/pubmed/34326249
http://dx.doi.org/10.1073/pnas.2023856118
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author Ayaz, Cihan
Tepper, Lucas
Brünig, Florian N.
Kappler, Julian
Daldrop, Jan O.
Netz, Roland R.
author_facet Ayaz, Cihan
Tepper, Lucas
Brünig, Florian N.
Kappler, Julian
Daldrop, Jan O.
Netz, Roland R.
author_sort Ayaz, Cihan
collection PubMed
description We extract the folding free energy landscape and the time-dependent friction function, the two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular dynamics (MD) simulations of the [Formula: see text]-helix forming polypeptide [Formula: see text] for a one-dimensional reaction coordinate based on the sum of the native H-bond distances. Folding and unfolding times from numerical integration of the GLE agree accurately with MD results, which demonstrate the robustness of our GLE-based non-Markovian model. In contrast, Markovian models do not accurately describe the peptide kinetics and in particular, cannot reproduce the folding and unfolding kinetics simultaneously, even if a spatially dependent friction profile is used. Analysis of the GLE demonstrates that memory effects in the friction significantly speed up peptide folding and unfolding kinetics, as predicted by the Grote–Hynes theory, and are the cause of anomalous diffusion in configuration space. Our methods are applicable to any reaction coordinate and in principle, also to experimental trajectories from single-molecule experiments. Our results demonstrate that a consistent description of protein-folding dynamics must account for memory friction effects.
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spelling pubmed-83468792021-08-23 Non-Markovian modeling of protein folding Ayaz, Cihan Tepper, Lucas Brünig, Florian N. Kappler, Julian Daldrop, Jan O. Netz, Roland R. Proc Natl Acad Sci U S A Physical Sciences We extract the folding free energy landscape and the time-dependent friction function, the two ingredients of the generalized Langevin equation (GLE), from explicit-water molecular dynamics (MD) simulations of the [Formula: see text]-helix forming polypeptide [Formula: see text] for a one-dimensional reaction coordinate based on the sum of the native H-bond distances. Folding and unfolding times from numerical integration of the GLE agree accurately with MD results, which demonstrate the robustness of our GLE-based non-Markovian model. In contrast, Markovian models do not accurately describe the peptide kinetics and in particular, cannot reproduce the folding and unfolding kinetics simultaneously, even if a spatially dependent friction profile is used. Analysis of the GLE demonstrates that memory effects in the friction significantly speed up peptide folding and unfolding kinetics, as predicted by the Grote–Hynes theory, and are the cause of anomalous diffusion in configuration space. Our methods are applicable to any reaction coordinate and in principle, also to experimental trajectories from single-molecule experiments. Our results demonstrate that a consistent description of protein-folding dynamics must account for memory friction effects. National Academy of Sciences 2021-08-03 2021-07-29 /pmc/articles/PMC8346879/ /pubmed/34326249 http://dx.doi.org/10.1073/pnas.2023856118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Physical Sciences
Ayaz, Cihan
Tepper, Lucas
Brünig, Florian N.
Kappler, Julian
Daldrop, Jan O.
Netz, Roland R.
Non-Markovian modeling of protein folding
title Non-Markovian modeling of protein folding
title_full Non-Markovian modeling of protein folding
title_fullStr Non-Markovian modeling of protein folding
title_full_unstemmed Non-Markovian modeling of protein folding
title_short Non-Markovian modeling of protein folding
title_sort non-markovian modeling of protein folding
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8346879/
https://www.ncbi.nlm.nih.gov/pubmed/34326249
http://dx.doi.org/10.1073/pnas.2023856118
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