Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

The catalytic efficiency of many enzymes is lower than the theoretical maximum. Here, the authors combine genome-scale metabolic modeling with population genetics models to simulate enzyme evolution, and find that strong epistasis limits turnover numbers due to diminishing returns of fitness gains.

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Detalles Bibliográficos
Autores principales:	David Heckmann, Daniel C. Zielinski, Bernhard O. Palsson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4f3a3407855745988954321965d01387
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spelling	oai:doaj.org-article:4f3a3407855745988954321965d013872021-12-02T17:32:46ZModeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates10.1038/s41467-018-07649-12041-1723https://doaj.org/article/4f3a3407855745988954321965d013872018-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07649-1https://doaj.org/toc/2041-1723The catalytic efficiency of many enzymes is lower than the theoretical maximum. Here, the authors combine genome-scale metabolic modeling with population genetics models to simulate enzyme evolution, and find that strong epistasis limits turnover numbers due to diminishing returns of fitness gains.David HeckmannDaniel C. ZielinskiBernhard O. PalssonNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q David Heckmann Daniel C. Zielinski Bernhard O. Palsson Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
description	The catalytic efficiency of many enzymes is lower than the theoretical maximum. Here, the authors combine genome-scale metabolic modeling with population genetics models to simulate enzyme evolution, and find that strong epistasis limits turnover numbers due to diminishing returns of fitness gains.
format	article
author	David Heckmann Daniel C. Zielinski Bernhard O. Palsson
author_facet	David Heckmann Daniel C. Zielinski Bernhard O. Palsson
author_sort	David Heckmann
title	Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
title_short	Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
title_full	Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
title_fullStr	Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
title_full_unstemmed	Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
title_sort	modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4f3a3407855745988954321965d01387
work_keys_str_mv	AT davidheckmann modelinggenomewideenzymeevolutionpredictsstrongepistasisunderlyingcatalyticturnoverrates AT danielczielinski modelinggenomewideenzymeevolutionpredictsstrongepistasisunderlyingcatalyticturnoverrates AT bernhardopalsson modelinggenomewideenzymeevolutionpredictsstrongepistasisunderlyingcatalyticturnoverrates
_version_	1718380184271323136

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

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