DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

Sanchez-Garcia et al. present DeepEMhancer, a deep learning-based method that can automatically perform post-processing of raw cryo-electron microscopy density maps. The authors report that DeepEMhancer globally improves local quality of density maps, and may represent a useful tool for novel struct...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ruben Sanchez-Garcia, Josue Gomez-Blanco, Ana Cuervo, Jose Maria Carazo, Carlos Oscar S. Sorzano, Javier Vargas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d8dd5236e640466ead393144dcbe902e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d8dd5236e640466ead393144dcbe902e
record_format	dspace
spelling	oai:doaj.org-article:d8dd5236e640466ead393144dcbe902e2021-12-02T15:33:09ZDeepEMhancer: a deep learning solution for cryo-EM volume post-processing10.1038/s42003-021-02399-12399-3642https://doaj.org/article/d8dd5236e640466ead393144dcbe902e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02399-1https://doaj.org/toc/2399-3642Sanchez-Garcia et al. present DeepEMhancer, a deep learning-based method that can automatically perform post-processing of raw cryo-electron microscopy density maps. The authors report that DeepEMhancer globally improves local quality of density maps, and may represent a useful tool for novel structures where PDB models are not readily available.Ruben Sanchez-GarciaJosue Gomez-BlancoAna CuervoJose Maria CarazoCarlos Oscar S. SorzanoJavier VargasNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ruben Sanchez-Garcia Josue Gomez-Blanco Ana Cuervo Jose Maria Carazo Carlos Oscar S. Sorzano Javier Vargas DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
description	Sanchez-Garcia et al. present DeepEMhancer, a deep learning-based method that can automatically perform post-processing of raw cryo-electron microscopy density maps. The authors report that DeepEMhancer globally improves local quality of density maps, and may represent a useful tool for novel structures where PDB models are not readily available.
format	article
author	Ruben Sanchez-Garcia Josue Gomez-Blanco Ana Cuervo Jose Maria Carazo Carlos Oscar S. Sorzano Javier Vargas
author_facet	Ruben Sanchez-Garcia Josue Gomez-Blanco Ana Cuervo Jose Maria Carazo Carlos Oscar S. Sorzano Javier Vargas
author_sort	Ruben Sanchez-Garcia
title	DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
title_short	DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
title_full	DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
title_fullStr	DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
title_full_unstemmed	DeepEMhancer: a deep learning solution for cryo-EM volume post-processing
title_sort	deepemhancer: a deep learning solution for cryo-em volume post-processing
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d8dd5236e640466ead393144dcbe902e
work_keys_str_mv	AT rubensanchezgarcia deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing AT josuegomezblanco deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing AT anacuervo deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing AT josemariacarazo deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing AT carlososcarssorzano deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing AT javiervargas deepemhanceradeeplearningsolutionforcryoemvolumepostprocessing
_version_	1718387112900820992

DeepEMhancer: a deep learning solution for cryo-EM volume post-processing

Ejemplares similares