Deciphering protein evolution and fitness landscapes with latent space models

Multiple sequence alignments of proteins carry information about evolution, the protein’s fitness landscape and its stability in the face of mutations. Here, the authors demonstrate the utility of latent space models learned using variational autoencoders to infer these properties from sequences.

Guardado en:

Detalles Bibliográficos
Autores principales:	Xinqiang Ding, Zhengting Zou, Charles L. Brooks III
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/be97bb08e16b437b9a2c5f2e3d8550b1
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:be97bb08e16b437b9a2c5f2e3d8550b1
record_format	dspace
spelling	oai:doaj.org-article:be97bb08e16b437b9a2c5f2e3d8550b12021-12-02T17:01:46ZDeciphering protein evolution and fitness landscapes with latent space models10.1038/s41467-019-13633-02041-1723https://doaj.org/article/be97bb08e16b437b9a2c5f2e3d8550b12019-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13633-0https://doaj.org/toc/2041-1723Multiple sequence alignments of proteins carry information about evolution, the protein’s fitness landscape and its stability in the face of mutations. Here, the authors demonstrate the utility of latent space models learned using variational autoencoders to infer these properties from sequences.Xinqiang DingZhengting ZouCharles L. Brooks IIINature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-13 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xinqiang Ding Zhengting Zou Charles L. Brooks III Deciphering protein evolution and fitness landscapes with latent space models
description	Multiple sequence alignments of proteins carry information about evolution, the protein’s fitness landscape and its stability in the face of mutations. Here, the authors demonstrate the utility of latent space models learned using variational autoencoders to infer these properties from sequences.
format	article
author	Xinqiang Ding Zhengting Zou Charles L. Brooks III
author_facet	Xinqiang Ding Zhengting Zou Charles L. Brooks III
author_sort	Xinqiang Ding
title	Deciphering protein evolution and fitness landscapes with latent space models
title_short	Deciphering protein evolution and fitness landscapes with latent space models
title_full	Deciphering protein evolution and fitness landscapes with latent space models
title_fullStr	Deciphering protein evolution and fitness landscapes with latent space models
title_full_unstemmed	Deciphering protein evolution and fitness landscapes with latent space models
title_sort	deciphering protein evolution and fitness landscapes with latent space models
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/be97bb08e16b437b9a2c5f2e3d8550b1
work_keys_str_mv	AT xinqiangding decipheringproteinevolutionandfitnesslandscapeswithlatentspacemodels AT zhengtingzou decipheringproteinevolutionandfitnesslandscapeswithlatentspacemodels AT charleslbrooksiii decipheringproteinevolutionandfitnesslandscapeswithlatentspacemodels
_version_	1718382049421688832

Deciphering protein evolution and fitness landscapes with latent space models

Ejemplares similares