Analysis of the confidence in the prediction of the protein folding by artificial intelligence

6 p.-4 fig.

Detalhes bibliográficos
Autores: Tejera-Nevado, Paloma, Serrano, Emilio, González-Herrero, Ana, Bermejo, Rodrigo, Rodríguez-González, Alejandro
Tipo de documento: outro
Estado:Versión aceptada para publicación
Data de publicação:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositório:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/331465
Acesso em linha:http://hdl.handle.net/10261/331465
Access Level:Acceso aberto
Palavra-chave:Protein structure prediction
Machine learning metrics
Model confidence
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spelling Analysis of the confidence in the prediction of the protein folding by artificial intelligenceTejera-Nevado, PalomaSerrano, EmilioGonzález-Herrero, AnaBermejo, RodrigoRodríguez-González, AlejandroProtein structure predictionMachine learning metricsModel confidence6 p.-4 fig.The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function.This work is a result of the project "Data-driven drug repositioning applying graph neural networks (3DR-GNN)", that is being developed under grant "PID2021-122659OB-I00" from the Spanish Ministerio de Ciencia e Innovación. This work was funded partially by Knowledge Spaces project (Grant PID2020-118274RB-I00 funded by MCIN/AEI/10.13039/501100011033)Peer reviewedSpringerMinisterio de Ciencia e Innovación (España)Tejera-Nevado, Paloma [0000-0003-0342-6640]Serrano, Emilio [0000-0001-7587-0703]González-Herrero, Ana [0000-0003-2014-563X]Bermejo, Rodrigo [0000-0002-2692-7045]Rodríguez-González, Alejandro [0000-0001-8801-4762]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232023info:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_3248Postprintinfo:eu-repo/semantics/acceptedVersioninfo:eu-repo/semantics/bookParthttp://hdl.handle.net/10261/331465reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118274RB-I00https://doi.org/10.1007/978-3-031-38079-2_9Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3314652026-05-22T06:33:51Z
dc.title.none.fl_str_mv Analysis of the confidence in the prediction of the protein folding by artificial intelligence
title Analysis of the confidence in the prediction of the protein folding by artificial intelligence
spellingShingle Analysis of the confidence in the prediction of the protein folding by artificial intelligence
Tejera-Nevado, Paloma
Protein structure prediction
Machine learning metrics
Model confidence
title_short Analysis of the confidence in the prediction of the protein folding by artificial intelligence
title_full Analysis of the confidence in the prediction of the protein folding by artificial intelligence
title_fullStr Analysis of the confidence in the prediction of the protein folding by artificial intelligence
title_full_unstemmed Analysis of the confidence in the prediction of the protein folding by artificial intelligence
title_sort Analysis of the confidence in the prediction of the protein folding by artificial intelligence
dc.creator.none.fl_str_mv Tejera-Nevado, Paloma
Serrano, Emilio
González-Herrero, Ana
Bermejo, Rodrigo
Rodríguez-González, Alejandro
author Tejera-Nevado, Paloma
author_facet Tejera-Nevado, Paloma
Serrano, Emilio
González-Herrero, Ana
Bermejo, Rodrigo
Rodríguez-González, Alejandro
author_role author
author2 Serrano, Emilio
González-Herrero, Ana
Bermejo, Rodrigo
Rodríguez-González, Alejandro
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Tejera-Nevado, Paloma [0000-0003-0342-6640]
Serrano, Emilio [0000-0001-7587-0703]
González-Herrero, Ana [0000-0003-2014-563X]
Bermejo, Rodrigo [0000-0002-2692-7045]
Rodríguez-González, Alejandro [0000-0001-8801-4762]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Protein structure prediction
Machine learning metrics
Model confidence
topic Protein structure prediction
Machine learning metrics
Model confidence
description 6 p.-4 fig.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/other
http://purl.org/coar/resource_type/c_3248
Postprint
info:eu-repo/semantics/acceptedVersion
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format other
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/331465
url http://hdl.handle.net/10261/331465
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118274RB-I00
https://doi.org/10.1007/978-3-031-38079-2_9
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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
repository.name.fl_str_mv
repository.mail.fl_str_mv
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