Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.

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Autores principales:	David Heckmann, Colton J. Lloyd, Nathan Mih, Yuanchi Ha, Daniel C. Zielinski, Zachary B. Haiman, Abdelmoneim Amer Desouki, Martin J. Lercher, Bernhard O. Palsson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4253a943492247b28ad5cd46f32d93b3
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spelling	oai:doaj.org-article:4253a943492247b28ad5cd46f32d93b32021-12-02T17:31:46ZMachine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models10.1038/s41467-018-07652-62041-1723https://doaj.org/article/4253a943492247b28ad5cd46f32d93b32018-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07652-6https://doaj.org/toc/2041-1723Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.David HeckmannColton J. LloydNathan MihYuanchi HaDaniel C. ZielinskiZachary B. HaimanAbdelmoneim Amer DesoukiMartin J. LercherBernhard O. PalssonNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q David Heckmann Colton J. Lloyd Nathan Mih Yuanchi Ha Daniel C. Zielinski Zachary B. Haiman Abdelmoneim Amer Desouki Martin J. Lercher Bernhard O. Palsson Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
description	Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances their predictive accuracy.
format	article
author	David Heckmann Colton J. Lloyd Nathan Mih Yuanchi Ha Daniel C. Zielinski Zachary B. Haiman Abdelmoneim Amer Desouki Martin J. Lercher Bernhard O. Palsson
author_facet	David Heckmann Colton J. Lloyd Nathan Mih Yuanchi Ha Daniel C. Zielinski Zachary B. Haiman Abdelmoneim Amer Desouki Martin J. Lercher Bernhard O. Palsson
author_sort	David Heckmann
title	Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
title_short	Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
title_full	Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
title_fullStr	Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
title_full_unstemmed	Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
title_sort	machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/4253a943492247b28ad5cd46f32d93b3
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Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

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