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

Cryo-electron microscopy (cryo-EM) maps are among the most valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed before modeling in order to improve their interpretability. To that end, appro...

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Autores: Sánchez-García, Rubén, Gómez-Blanco, Josué, Cuervo, Ana, Carazo, José M., Sorzano, Carlos Óscar S., Vargas, Javier
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/214335
Acceso en línea:http://hdl.handle.net/10261/214335
Access Level:acceso abierto
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spelling DeepEMhacer: a deep learning solution for cryo-EM volume post-processingSánchez-García, RubénGómez-Blanco, JosuéCuervo, AnaCarazo, José M.Sorzano, Carlos Óscar S.Vargas, JavierCryo-electron microscopy (cryo-EM) maps are among the most valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed before modeling in order to improve their interpretability. To that end, approaches based on B-factor correction are the most popular choices, yet they suffer from some limitations such as the fact that the correction is applied globally, ignoring the presence of heterogeneity in the map local quality that cryo-EM reconstructions tend to exhibit. With the aim of overcoming these limitations, here we present DeepEMhacer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental cryo-EM maps and maps sharpened by LocScape using their respective atomic models, DeepEMhacer has automatically learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhacer has been evaluated on a testing set of 20 different experimental maps, showing its ability to obtain much cleaner and detailed versions of the experimental maps, thus, improving their interpretability. Additionally, we have illustrated the benefits of DeepEMhacer with a use case in which the structure of the SARS-CoV 2 RNA polymerase is improved.This work is supported by the the Comunidad de Madrid through grant CAM (S2017/BMD-3817), the Spanish Ministry of Economy and Competitiveness (BIO2016-76400-R). J.V. acknowledges economical support from the Ramón y Cajal 2018 program (RYC2018-024087- I). R.S. is recipient of an FPU fellowship.Peer reviewedBioRxivComunidad de MadridMinisterio de Economía y Competitividad (España)Ministerio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Sánchez-García, Ruben [0000-0001-6156-3542]Gómez-Blanco, Josué [0000-0002-6168-3859]Cuervo, Ana [0000-0001-9414-503X]Carazo, José M. [0000-0003-0788-8447]Sorzano, Carlos Óscar S. [0000-0002-9473-283X]Vargas, Javier [0000-0001-7519-6106]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Preprintinfo:eu-repo/semantics/submittedVersionhttp://hdl.handle.net/10261/214335reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#S2017/BMD-3817/TomoXLiver-CMinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BIO2016-76400-Rinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RYC2018-024087- IRYC2018-024087-I/AEI/10.13039/501100011033https://doi.org/10.1101/2020.06.12.148296Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2143352026-05-22T06:33:51Z
dc.title.none.fl_str_mv DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
title DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
spellingShingle DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
Sánchez-García, Rubén
title_short DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
title_full DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
title_fullStr DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
title_full_unstemmed DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
title_sort DeepEMhacer: a deep learning solution for cryo-EM volume post-processing
dc.creator.none.fl_str_mv Sánchez-García, Rubén
Gómez-Blanco, Josué
Cuervo, Ana
Carazo, José M.
Sorzano, Carlos Óscar S.
Vargas, Javier
author Sánchez-García, Rubén
author_facet Sánchez-García, Rubén
Gómez-Blanco, Josué
Cuervo, Ana
Carazo, José M.
Sorzano, Carlos Óscar S.
Vargas, Javier
author_role author
author2 Gómez-Blanco, Josué
Cuervo, Ana
Carazo, José M.
Sorzano, Carlos Óscar S.
Vargas, Javier
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Comunidad de Madrid
Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Sánchez-García, Ruben [0000-0001-6156-3542]
Gómez-Blanco, Josué [0000-0002-6168-3859]
Cuervo, Ana [0000-0001-9414-503X]
Carazo, José M. [0000-0003-0788-8447]
Sorzano, Carlos Óscar S. [0000-0002-9473-283X]
Vargas, Javier [0000-0001-7519-6106]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
description Cryo-electron microscopy (cryo-EM) maps are among the most valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed before modeling in order to improve their interpretability. To that end, approaches based on B-factor correction are the most popular choices, yet they suffer from some limitations such as the fact that the correction is applied globally, ignoring the presence of heterogeneity in the map local quality that cryo-EM reconstructions tend to exhibit. With the aim of overcoming these limitations, here we present DeepEMhacer, a deep learning approach designed to perform automatic post-processing of cryo-EM maps. Trained on a dataset of pairs of experimental cryo-EM maps and maps sharpened by LocScape using their respective atomic models, DeepEMhacer has automatically learned how to post-process experimental maps performing masking-like and sharpening-like operations in a single step. DeepEMhacer has been evaluated on a testing set of 20 different experimental maps, showing its ability to obtain much cleaner and detailed versions of the experimental maps, thus, improving their interpretability. Additionally, we have illustrated the benefits of DeepEMhacer with a use case in which the structure of the SARS-CoV 2 RNA polymerase is improved.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Preprint
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/214335
url http://hdl.handle.net/10261/214335
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
S2017/BMD-3817/TomoXLiver-CM
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/BIO2016-76400-R
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RYC2018-024087- I
RYC2018-024087-I/AEI/10.13039/501100011033
https://doi.org/10.1101/2020.06.12.148296
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioRxiv
publisher.none.fl_str_mv BioRxiv
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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