The dynamic landscape of peptide activity prediction

Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood-brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgra...

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Autores: Bárcenas, Oriol|||0000-0002-8439-4005, Pintado-Grima, Carlos|||0000-0002-8544-959X, Sidorczuk, Katarzyna|||0000-0001-6576-9054, Teufel, Felix, Nielsen, Henrik, Ventura, Salvador|||0000-0002-9652-6351, Burdukiewicz, Michał|||0000-0001-8926-582X
Tipo de documento: artigo
Data de publicação:2022
País:España
Recursos:Universitat Autònoma de Barcelona
Repositório:Dipòsit Digital de Documents de la UAB
Idioma:inglês
OAI Identifier:oai:ddd.uab.cat:269564
Acesso em linha:https://ddd.uab.cat/record/269564
https://dx.doi.org/urn:doi:10.1016/j.csbj.2022.11.043
Access Level:Acceso aberto
Palavra-chave:Peptides
Activity
Prediction
Functional peptides
Machine learning
Deep learning
Reproducibility
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spelling The dynamic landscape of peptide activity predictionBárcenas, Oriol|||0000-0002-8439-4005Pintado-Grima, Carlos|||0000-0002-8544-959XSidorczuk, Katarzyna|||0000-0001-6576-9054Teufel, FelixNielsen, HenrikVentura, Salvador|||0000-0002-9652-6351Burdukiewicz, Michał|||0000-0001-8926-582XPeptidesActivityPredictionFunctional peptidesMachine learningDeep learningReproducibilityPeptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood-brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity in silico, generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool. This minireview aims at providing a systematic and accessible analysis of the complex ecosystem of peptide activity prediction, showcasing the variability of existing models for peptide assessment, their domain specialization and popularity. Moreover, we also assess the reproducibility of such bioinformatics tools and describe tendencies observed in their development. The list of tools is available under. 22022-01-0120222022-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/269564https://dx.doi.org/urn:doi:10.1016/j.csbj.2022.11.043reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2021/FI_B-00087Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2019-105017RB-I00open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:2695642026-06-06T12:50:31Z
dc.title.none.fl_str_mv The dynamic landscape of peptide activity prediction
title The dynamic landscape of peptide activity prediction
spellingShingle The dynamic landscape of peptide activity prediction
Bárcenas, Oriol|||0000-0002-8439-4005
Peptides
Activity
Prediction
Functional peptides
Machine learning
Deep learning
Reproducibility
title_short The dynamic landscape of peptide activity prediction
title_full The dynamic landscape of peptide activity prediction
title_fullStr The dynamic landscape of peptide activity prediction
title_full_unstemmed The dynamic landscape of peptide activity prediction
title_sort The dynamic landscape of peptide activity prediction
dc.creator.none.fl_str_mv Bárcenas, Oriol|||0000-0002-8439-4005
Pintado-Grima, Carlos|||0000-0002-8544-959X
Sidorczuk, Katarzyna|||0000-0001-6576-9054
Teufel, Felix
Nielsen, Henrik
Ventura, Salvador|||0000-0002-9652-6351
Burdukiewicz, Michał|||0000-0001-8926-582X
author Bárcenas, Oriol|||0000-0002-8439-4005
author_facet Bárcenas, Oriol|||0000-0002-8439-4005
Pintado-Grima, Carlos|||0000-0002-8544-959X
Sidorczuk, Katarzyna|||0000-0001-6576-9054
Teufel, Felix
Nielsen, Henrik
Ventura, Salvador|||0000-0002-9652-6351
Burdukiewicz, Michał|||0000-0001-8926-582X
author_role author
author2 Pintado-Grima, Carlos|||0000-0002-8544-959X
Sidorczuk, Katarzyna|||0000-0001-6576-9054
Teufel, Felix
Nielsen, Henrik
Ventura, Salvador|||0000-0002-9652-6351
Burdukiewicz, Michał|||0000-0001-8926-582X
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Peptides
Activity
Prediction
Functional peptides
Machine learning
Deep learning
Reproducibility
topic Peptides
Activity
Prediction
Functional peptides
Machine learning
Deep learning
Reproducibility
description Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood-brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity in silico, generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool. This minireview aims at providing a systematic and accessible analysis of the complex ecosystem of peptide activity prediction, showcasing the variability of existing models for peptide assessment, their domain specialization and popularity. Moreover, we also assess the reproducibility of such bioinformatics tools and describe tendencies observed in their development. The list of tools is available under.
publishDate 2022
dc.date.none.fl_str_mv 2
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/269564
https://dx.doi.org/urn:doi:10.1016/j.csbj.2022.11.043
url https://ddd.uab.cat/record/269564
https://dx.doi.org/urn:doi:10.1016/j.csbj.2022.11.043
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agència de Gestió d'Ajuts Universitaris i de Recerca https://doi.org/10.13039/501100003030 2021/FI_B-00087
Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2019-105017RB-I00
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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