Mapping the glycosyltransferase fold landscape using interpretable deep learning

Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new fold...

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Autores principales:	Rahil Taujale, Zhongliang Zhou, Wayland Yeung, Kelley W. Moremen, Sheng Li, Natarajan Kannan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f5c17c58d05e4400857aec9c85fdb18c
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id	oai:doaj.org-article:f5c17c58d05e4400857aec9c85fdb18c
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spelling	oai:doaj.org-article:f5c17c58d05e4400857aec9c85fdb18c2021-12-02T19:16:47ZMapping the glycosyltransferase fold landscape using interpretable deep learning10.1038/s41467-021-25975-92041-1723https://doaj.org/article/f5c17c58d05e4400857aec9c85fdb18c2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25975-9https://doaj.org/toc/2041-1723Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new folds.Rahil TaujaleZhongliang ZhouWayland YeungKelley W. MoremenSheng LiNatarajan KannanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Rahil Taujale Zhongliang Zhou Wayland Yeung Kelley W. Moremen Sheng Li Natarajan Kannan Mapping the glycosyltransferase fold landscape using interpretable deep learning
description	Glycosyltransferases (GT) are proteins that display extensive sequence and functional variation on a subset of 3D folds. Here, the authors use interpretable deep learning to predict 3D folds from sequence without the need for sequence alignment, which also enables the prediction of GTs with new folds.
format	article
author	Rahil Taujale Zhongliang Zhou Wayland Yeung Kelley W. Moremen Sheng Li Natarajan Kannan
author_facet	Rahil Taujale Zhongliang Zhou Wayland Yeung Kelley W. Moremen Sheng Li Natarajan Kannan
author_sort	Rahil Taujale
title	Mapping the glycosyltransferase fold landscape using interpretable deep learning
title_short	Mapping the glycosyltransferase fold landscape using interpretable deep learning
title_full	Mapping the glycosyltransferase fold landscape using interpretable deep learning
title_fullStr	Mapping the glycosyltransferase fold landscape using interpretable deep learning
title_full_unstemmed	Mapping the glycosyltransferase fold landscape using interpretable deep learning
title_sort	mapping the glycosyltransferase fold landscape using interpretable deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f5c17c58d05e4400857aec9c85fdb18c
work_keys_str_mv	AT rahiltaujale mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning AT zhongliangzhou mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning AT waylandyeung mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning AT kelleywmoremen mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning AT shengli mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning AT natarajankannan mappingtheglycosyltransferasefoldlandscapeusinginterpretabledeeplearning
_version_	1718376948492664832

Mapping the glycosyltransferase fold landscape using interpretable deep learning

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