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Quantifying the limits of transition state theory in enzymatic catalysis

While being one of the most popular reaction rate theories, the applicability of transition state theory to the study of enzymatic reactions has been often challenged. The complex dynamic nature of the protein environment raised the question about the validity of the nonrecrossing hypothesis, a corn...

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Autores principales: Zinovjev, Kirill, Tuñón, Iñaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703300/
https://www.ncbi.nlm.nih.gov/pubmed/29101125
http://dx.doi.org/10.1073/pnas.1710820114
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author Zinovjev, Kirill
Tuñón, Iñaki
author_facet Zinovjev, Kirill
Tuñón, Iñaki
author_sort Zinovjev, Kirill
collection PubMed
description While being one of the most popular reaction rate theories, the applicability of transition state theory to the study of enzymatic reactions has been often challenged. The complex dynamic nature of the protein environment raised the question about the validity of the nonrecrossing hypothesis, a cornerstone in this theory. We present a computational strategy to quantify the error associated to transition state theory from the number of recrossings observed at the equicommittor, which is the best possible dividing surface. Application of a direct multidimensional transition state optimization to the hydride transfer step in human dihydrofolate reductase shows that both the participation of the protein degrees of freedom in the reaction coordinate and the error associated to the nonrecrossing hypothesis are small. Thus, the use of transition state theory, even with simplified reaction coordinates, provides a good theoretical framework for the study of enzymatic catalysis.
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spelling pubmed-57033002017-11-28 Quantifying the limits of transition state theory in enzymatic catalysis Zinovjev, Kirill Tuñón, Iñaki Proc Natl Acad Sci U S A Physical Sciences While being one of the most popular reaction rate theories, the applicability of transition state theory to the study of enzymatic reactions has been often challenged. The complex dynamic nature of the protein environment raised the question about the validity of the nonrecrossing hypothesis, a cornerstone in this theory. We present a computational strategy to quantify the error associated to transition state theory from the number of recrossings observed at the equicommittor, which is the best possible dividing surface. Application of a direct multidimensional transition state optimization to the hydride transfer step in human dihydrofolate reductase shows that both the participation of the protein degrees of freedom in the reaction coordinate and the error associated to the nonrecrossing hypothesis are small. Thus, the use of transition state theory, even with simplified reaction coordinates, provides a good theoretical framework for the study of enzymatic catalysis. National Academy of Sciences 2017-11-21 2017-11-03 /pmc/articles/PMC5703300/ /pubmed/29101125 http://dx.doi.org/10.1073/pnas.1710820114 Text en Copyright © 2017 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
Zinovjev, Kirill
Tuñón, Iñaki
Quantifying the limits of transition state theory in enzymatic catalysis
title Quantifying the limits of transition state theory in enzymatic catalysis
title_full Quantifying the limits of transition state theory in enzymatic catalysis
title_fullStr Quantifying the limits of transition state theory in enzymatic catalysis
title_full_unstemmed Quantifying the limits of transition state theory in enzymatic catalysis
title_short Quantifying the limits of transition state theory in enzymatic catalysis
title_sort quantifying the limits of transition state theory in enzymatic catalysis
topic Physical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5703300/
https://www.ncbi.nlm.nih.gov/pubmed/29101125
http://dx.doi.org/10.1073/pnas.1710820114
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