Machine learning differentiates enzymatic and non-enzymatic metals in proteins

The authors generate the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. They use this dataset to train a decision-tree ensemble machine learning algorithm that allows them to distinguish between catalytic and non-catalytic metal sites. The computational model...

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Detalles Bibliográficos
Autores principales:	Ryan Feehan, Meghan W. Franklin, Joanna S. G. Slusky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/aecd91b4c8984f009faa432576d01db6
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spelling	oai:doaj.org-article:aecd91b4c8984f009faa432576d01db62021-12-02T17:24:21ZMachine learning differentiates enzymatic and non-enzymatic metals in proteins10.1038/s41467-021-24070-32041-1723https://doaj.org/article/aecd91b4c8984f009faa432576d01db62021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24070-3https://doaj.org/toc/2041-1723The authors generate the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. They use this dataset to train a decision-tree ensemble machine learning algorithm that allows them to distinguish between catalytic and non-catalytic metal sites. The computational model described here could also be useful for the identification of new enzymatic mechanisms and de novo enzyme design.Ryan FeehanMeghan W. FranklinJoanna S. G. SluskyNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ryan Feehan Meghan W. Franklin Joanna S. G. Slusky Machine learning differentiates enzymatic and non-enzymatic metals in proteins
description	The authors generate the largest structural dataset of enzymatic and non-enzymatic metalloprotein sites to date. They use this dataset to train a decision-tree ensemble machine learning algorithm that allows them to distinguish between catalytic and non-catalytic metal sites. The computational model described here could also be useful for the identification of new enzymatic mechanisms and de novo enzyme design.
format	article
author	Ryan Feehan Meghan W. Franklin Joanna S. G. Slusky
author_facet	Ryan Feehan Meghan W. Franklin Joanna S. G. Slusky
author_sort	Ryan Feehan
title	Machine learning differentiates enzymatic and non-enzymatic metals in proteins
title_short	Machine learning differentiates enzymatic and non-enzymatic metals in proteins
title_full	Machine learning differentiates enzymatic and non-enzymatic metals in proteins
title_fullStr	Machine learning differentiates enzymatic and non-enzymatic metals in proteins
title_full_unstemmed	Machine learning differentiates enzymatic and non-enzymatic metals in proteins
title_sort	machine learning differentiates enzymatic and non-enzymatic metals in proteins
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/aecd91b4c8984f009faa432576d01db6
work_keys_str_mv	AT ryanfeehan machinelearningdifferentiatesenzymaticandnonenzymaticmetalsinproteins AT meghanwfranklin machinelearningdifferentiatesenzymaticandnonenzymaticmetalsinproteins AT joannasgslusky machinelearningdifferentiatesenzymaticandnonenzymaticmetalsinproteins
_version_	1718380930244018176

Machine learning differentiates enzymatic and non-enzymatic metals in proteins

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