Discovery of antimicrobial peptides in the global microbiome with machine learning

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and...

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Authors: Santos-Júnior, Célio Dias, Torres, Marcelo D. T., Duan, Yiqian, Rodríguez Del Río, Álvaro, Schmidt, Thomas S B, Chong, Hui, Fullam, Anthony, Kuhn, Michael, Zhu, Chengkai, Houseman, Amy, Somborski, Jelena, Vines, Anna, Zhao, Xing-Ming, Bork, Peer, Huerta-Cepas, Jaime, Fuente-Núñez, César de la, Coelho, Luis Pedro
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/389013
Online Access:http://hdl.handle.net/10261/389013
https://api.elsevier.com/content/abstract/scopus_id/85195694353
Access Level:Open access
Keyword:Antibiotic discovery
Antibiotic resistance
Antimicrobial activity
Antimicrobial peptides
Global microbiome
Machine learning
Metagenomics
id ES_f5eab6820e493dd9806f93d00a283648
oai_identifier_str oai:digital.csic.es:10261/389013
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Discovery of antimicrobial peptides in the global microbiome with machine learning
title Discovery of antimicrobial peptides in the global microbiome with machine learning
spellingShingle Discovery of antimicrobial peptides in the global microbiome with machine learning
Santos-Júnior, Célio Dias
Antibiotic discovery
Antibiotic resistance
Antimicrobial activity
Antimicrobial peptides
Global microbiome
Machine learning
Metagenomics
title_short Discovery of antimicrobial peptides in the global microbiome with machine learning
title_full Discovery of antimicrobial peptides in the global microbiome with machine learning
title_fullStr Discovery of antimicrobial peptides in the global microbiome with machine learning
title_full_unstemmed Discovery of antimicrobial peptides in the global microbiome with machine learning
title_sort Discovery of antimicrobial peptides in the global microbiome with machine learning
dc.creator.none.fl_str_mv Santos-Júnior, Célio Dias
Torres, Marcelo D. T.
Duan, Yiqian
Rodríguez Del Río, Álvaro
Schmidt, Thomas S B
Chong, Hui
Fullam, Anthony
Kuhn, Michael
Zhu, Chengkai
Houseman, Amy
Somborski, Jelena
Vines, Anna
Zhao, Xing-Ming
Bork, Peer
Huerta-Cepas, Jaime
Fuente-Núñez, César de la
Coelho, Luis Pedro
author Santos-Júnior, Célio Dias
author_facet Santos-Júnior, Célio Dias
Torres, Marcelo D. T.
Duan, Yiqian
Rodríguez Del Río, Álvaro
Schmidt, Thomas S B
Chong, Hui
Fullam, Anthony
Kuhn, Michael
Zhu, Chengkai
Houseman, Amy
Somborski, Jelena
Vines, Anna
Zhao, Xing-Ming
Bork, Peer
Huerta-Cepas, Jaime
Fuente-Núñez, César de la
Coelho, Luis Pedro
author_role author
author2 Torres, Marcelo D. T.
Duan, Yiqian
Rodríguez Del Río, Álvaro
Schmidt, Thomas S B
Chong, Hui
Fullam, Anthony
Kuhn, Michael
Zhu, Chengkai
Houseman, Amy
Somborski, Jelena
Vines, Anna
Zhao, Xing-Ming
Bork, Peer
Huerta-Cepas, Jaime
Fuente-Núñez, César de la
Coelho, Luis Pedro
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Procter & Gamble
United Therapeutics Corporation
University of Pennsylvania
National Natural Science Foundation of China
Shanghai Science and Technology Committee
National Key Research and Development Program (China)
Shanghai Municipal Natural Science Foundation
Australian Research Council
AIChE Foundation
National Institutes of Health (US)
Defense Threat Reduction Agency (US)
Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Fundación la Caixa
European Commission
Santos-Júnior, Célio Dias [0000-0002-1974-1736]
Coelho, Luis Pedro [0000-0002-9280-7885]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Antibiotic discovery
Antibiotic resistance
Antimicrobial activity
Antimicrobial peptides
Global microbiome
Machine learning
Metagenomics
topic Antibiotic discovery
Antibiotic resistance
Antimicrobial activity
Antimicrobial peptides
Global microbiome
Machine learning
Metagenomics
description Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
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/10261/389013
https://api.elsevier.com/content/abstract/scopus_id/85195694353
url http://hdl.handle.net/10261/389013
https://api.elsevier.com/content/abstract/scopus_id/85195694353
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#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127210NB-I00
info:eu-repo/grantAgreement/EC/H2020/713673
Centro de Biotecnología y Genómica de Plantas, (CBGP)
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.cell.2024.05.013
https://doi.org/10.1016/j.cell.2024.05.013
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier BV
Cell Press
publisher.none.fl_str_mv Elsevier BV
Cell Press
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
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spelling Discovery of antimicrobial peptides in the global microbiome with machine learningSantos-Júnior, Célio DiasTorres, Marcelo D. T.Duan, YiqianRodríguez Del Río, ÁlvaroSchmidt, Thomas S BChong, HuiFullam, AnthonyKuhn, MichaelZhu, ChengkaiHouseman, AmySomborski, JelenaVines, AnnaZhao, Xing-MingBork, PeerHuerta-Cepas, JaimeFuente-Núñez, César de laCoelho, Luis PedroAntibiotic discoveryAntibiotic resistanceAntimicrobial activityAntimicrobial peptidesGlobal microbiomeMachine learningMetagenomicsNovel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.We thank Marija Dmitrijeva (University of Zurich) for her helpful comments on a previous version of the manuscript. We thank Kaylyn Tousignant (Queensland University of Technology) for her help editing the manuscript. We thank Georgina H. Joyce (Queensland University of Technology) for her help designing the graphical abstract. We thank members of the Coelho group and the de la Fuente Lab for insightful discussions. C.F.-N. holds a Presidential Professorship at the University of Pennsylvania and acknowledges funding from the Procter & Gamble Company, United Therapeutics, a BBRF Young Investigator Grant, the Nemirovsky Prize, the Penn Health-Tech Accelerator Award, Defense Threat Reduction Agency grants HDTRA11810041 and HDTRA1-23-1-0001, and the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania. We thank Dr. Mark Goulian for kindly donating the strains Escherichia coli AIC221 (Escherichia coli MG1655 phnE_2:FRT [control strain for AIC 222]) and Escherichia coli AIC222 (Escherichia coli MG1655 pmrA53 phnE_2:FRT [polymyxin-resistant]). This work was partly funded by the EMBL and the following grants: National Natural Science Foundation of China grants T2225015 and 61932008 (L.P.C. and X.-M.Z.); Shanghai Science and Technology Commission Program grant 23JS1410100 (L.P.C. and X.-M.Z.); National Key R&D Program of China grants 2023YFF1204800 and 2020YFA0712403 (L.P.C. and X.-M.Z.); Shanghai Municipal Science and Technology Major Project grant 2018SHZDZX01 (L.P.C. and X.-M.Z.); Lingang Laboratory and National Key Laboratory of Human Factors Engineering Joint Grant LG-TKN-202203-01 (X.-M.Z.); The Science and Technology Commission of Shanghai Municipality grant 22JC1410900 (L.P.C.); Australian Research Council grant FT230100724 (L.P.C.); the Langer Prize from the AIChE Foundation (C.F.-N.); National Institutes of Health grant R35GM138201 (C.F.-N.); Defense Threat Reduction Agency grant HDTRA1-21-1-0014 (C.F.-N.); PID2021-127210NB-I00, MCIN/AEI/10.13039/501100011033/FEDER, UE (J.H.-C.); 'la Caixa' Foundation ID 100010434, fellowship code LCF/BQ/DI18/11660009 (A.R.d.R.); and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 713673 (A.R.d.R.).Peer reviewedElsevier BVCell PressProcter & GambleUnited Therapeutics CorporationUniversity of PennsylvaniaNational Natural Science Foundation of ChinaShanghai Science and Technology CommitteeNational Key Research and Development Program (China)Shanghai Municipal Natural Science FoundationAustralian Research CouncilAIChE FoundationNational Institutes of Health (US)Defense Threat Reduction Agency (US)Agencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Fundación la CaixaEuropean CommissionSantos-Júnior, Célio Dias [0000-0002-1974-1736]Coelho, Luis Pedro [0000-0002-9280-7885]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/389013https://api.elsevier.com/content/abstract/scopus_id/85195694353reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127210NB-I00info:eu-repo/grantAgreement/EC/H2020/713673Centro de Biotecnología y Genómica de Plantas, (CBGP)The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.cell.2024.05.013https://doi.org/10.1016/j.cell.2024.05.013Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3890132026-05-22T06:33:51Z
score 15.198674

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