Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models

Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers...

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Detalhes bibliográficos
Autores: Lobo, Fernando, González, Maily Selena, Boto, Alicia, Pérez de la Lastra, José Manuel
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/313577
Acesso em linha:http://hdl.handle.net/10261/313577
Access Level:acceso abierto
Palavra-chave:Antimicrobial peptides
antifungal peptides
transfer learning
Machine learning
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spelling Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained ModelsLobo, FernandoGonzález, Maily SelenaBoto, AliciaPérez de la Lastra, José ManuelAntimicrobial peptidesantifungal peptidestransfer learningMachine learningPeptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.This work was financed by project RETOS-SELECTFIGHT (PID2020-116688RB-C21) of the Plan Estatal I + D, Ministry of Science, Spain (with FEDER funds) and previously by Fundación Caja Canarias, Project 2019SP43. F.L. acknowledges his research and transfer contract “Agustín de Bethancourt” at the University of La Laguna, sponsored by Cabildo de Tenerife, Program TF INNOVA 2016-21 (with MEDI & FDCAN Funds). M.G acknowledges her JAE Intro-ICU grant (Reference: JAEICU-21-IQM-20, Red Conexión Instituto de Química Médica) financed by the Conexión de Nanomedicina of the Spanish Research Council (CSIC). We also acknowledge support of the publication fee by CSIC Open Access Publication Support Initiative, through its Unit of Information Resources for Research (URICI).Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia e Innovación (España)European CommissionCaja CanariasUniversidad de La LagunaCabildo de TenerifeConsejo Superior de Investigaciones Científicas (España)CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/313577reponame: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/PID2020-116688RB-C21https://doi.org/10.3390/ijms241210270Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3135772026-05-22T06:33:51Z
dc.title.none.fl_str_mv Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
title Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
spellingShingle Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
Lobo, Fernando
Antimicrobial peptides
antifungal peptides
transfer learning
Machine learning
title_short Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
title_full Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
title_fullStr Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
title_full_unstemmed Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
title_sort Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
dc.creator.none.fl_str_mv Lobo, Fernando
González, Maily Selena
Boto, Alicia
Pérez de la Lastra, José Manuel
author Lobo, Fernando
author_facet Lobo, Fernando
González, Maily Selena
Boto, Alicia
Pérez de la Lastra, José Manuel
author_role author
author2 González, Maily Selena
Boto, Alicia
Pérez de la Lastra, José Manuel
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
European Commission
Caja Canarias
Universidad de La Laguna
Cabildo de Tenerife
Consejo Superior de Investigaciones Científicas (España)
CSIC - Unidad de Recursos de Información Científica para la Investigación (URICI)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Antimicrobial peptides
antifungal peptides
transfer learning
Machine learning
topic Antimicrobial peptides
antifungal peptides
transfer learning
Machine learning
description Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/313577
url http://hdl.handle.net/10261/313577
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/PID2020-116688RB-C21
https://doi.org/10.3390/ijms241210270
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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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score 15,811543

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