Improved protein structure refinement guided by deep learning based accuracy estimation

Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement com...

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Autores principales:	Naozumi Hiranuma, Hahnbeom Park, Minkyung Baek, Ivan Anishchenko, Justas Dauparas, David Baker
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/0ddf48c191ed4b229b81c3276e7def22
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id	oai:doaj.org-article:0ddf48c191ed4b229b81c3276e7def22
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spelling	oai:doaj.org-article:0ddf48c191ed4b229b81c3276e7def222021-12-02T15:52:37ZImproved protein structure refinement guided by deep learning based accuracy estimation10.1038/s41467-021-21511-x2041-1723https://doaj.org/article/0ddf48c191ed4b229b81c3276e7def222021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21511-xhttps://doaj.org/toc/2041-1723Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.Naozumi HiranumaHahnbeom ParkMinkyung BaekIvan AnishchenkoJustas DauparasDavid BakerNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Naozumi Hiranuma Hahnbeom Park Minkyung Baek Ivan Anishchenko Justas Dauparas David Baker Improved protein structure refinement guided by deep learning based accuracy estimation
description	Here the authors present DeepAccNet, a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, which are used to guide Rosetta protein structure refinement. Benchmarking suggests an improvement of accuracy prediction and refinement compared to other related state of the art methods.
format	article
author	Naozumi Hiranuma Hahnbeom Park Minkyung Baek Ivan Anishchenko Justas Dauparas David Baker
author_facet	Naozumi Hiranuma Hahnbeom Park Minkyung Baek Ivan Anishchenko Justas Dauparas David Baker
author_sort	Naozumi Hiranuma
title	Improved protein structure refinement guided by deep learning based accuracy estimation
title_short	Improved protein structure refinement guided by deep learning based accuracy estimation
title_full	Improved protein structure refinement guided by deep learning based accuracy estimation
title_fullStr	Improved protein structure refinement guided by deep learning based accuracy estimation
title_full_unstemmed	Improved protein structure refinement guided by deep learning based accuracy estimation
title_sort	improved protein structure refinement guided by deep learning based accuracy estimation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ddf48c191ed4b229b81c3276e7def22
work_keys_str_mv	AT naozumihiranuma improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation AT hahnbeompark improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation AT minkyungbaek improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation AT ivananishchenko improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation AT justasdauparas improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation AT davidbaker improvedproteinstructurerefinementguidedbydeeplearningbasedaccuracyestimation
_version_	1718385553843421184

Improved protein structure refinement guided by deep learning based accuracy estimation

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