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Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning

2021-01-14Journal article Restricted access
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dc.contributor.authorRibeiro Da Cunha, Bernardo
dc.contributor.authorFonseca, Luís P. P.
dc.contributor.authorCalado, Cecília
dc.date.accessioned2021-02-01T13:00:58Z
dc.date.available2021-02-01T13:00:58Z
dc.date.issued2021-01-14
dc.descriptionEste trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2017 do Instituto Politécnico de Lisboa. Código de referência IPL/2017/DrugsPlatf/ISEL
dc.description.abstractThe low rate of discovery and rapid spread of resistant pathogens have made antibiotic discovery a worldwide priority. In cell-based screening, the mechanism of action (MOA) is identified after antimicrobial activity. This increases rediscovery, impairs low potency candidate detection, and does not guide lead optimization. In this study, high-throughput Fourier-transform infrared (FTIR) spectroscopy was used to discriminate the MOA of 14 antibiotics at pathway, class, and individual antibiotic level. For that, the optimal combinations and parametrizations of spectral preprocessing were selected with cross-validated partial least squares discriminant analysis, to which various machine learning algorithms were applied. This coherently resulted in very good accuracies, independently of the algorithms, and at all levels of MOA. Particularly, an ensemble of subspace discriminants predicted the known pathway (98.6%), antibiotic classes (100%), and individual antibiotics (97.8%) with exceptional accuracy, and similar results were obtained for simulated novel MOA. Even at very low concentrations (1 mu g/mL) and growth inhibition (15%), over 70% pathway and class accuracy was achieved, suggesting FTIR spectroscopy can probe the grey chemical matter. Prediction of inhibitory effect was also examined, for which a squared exponential Gaussian process regression yielded a root mean square error of 0.33 and a R-2 of 0.92, indicating that metabolic alterations leading to growth inhibition are intrinsically reflected on FTIR spectra beyond cell density.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCUNHA, Bernardo Ribeiro da; FONSECA, Luís P.; CALADO, Cecília – Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning. Applied Microbiology and Biotechnology. ISSN 0175-7598. Vol. 105, N.º 3 (2021), pp. 1269-1286pt_PT
dc.identifier.doi10.1007/s00253-021-11102-7pt_PT
dc.identifier.eissn1432-0614
dc.identifier.issn0175-7598
dc.identifier.urihttp://hdl.handle.net/10400.21/12742
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationProjeto financiado no âmbito do Concurso de Projetos de Investigação, Desenvolvimento, Inovação & Criação Artística (IDI&CA) financiados pelo Instituto Politécnico de Lisboa. IPL/2017/DrugsPlatf/ISELpt_PT
dc.subjectAntibiotic discoverypt_PT
dc.subjectFourier-transform infrared (FTIR) spectroscopypt_PT
dc.subjectHigh-throughput screeningpt_PT
dc.subjectMechanism of action (MOA)pt_PT
dc.subjectAntimicrobial potencypt_PT
dc.titleSimultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBIO%2F69242%2F2006/PT
oaire.citation.endPage1286pt_PT
oaire.citation.issue3pt_PT
oaire.citation.startPage1269pt_PT
oaire.citation.titleApplied Microbiology and Biotechnologypt_PT
oaire.citation.volume105pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameRibeiro da Cunha
person.familyNameP. Fonseca
person.familyNameCalado
person.givenNameBernardo
person.givenNameLuis
person.givenNameCecília
person.identifier130332
person.identifier.ciencia-idEA1E-4BEA-A01E
person.identifier.ciencia-id951D-DF38-3397
person.identifier.ciencia-id9418-E320-3177
person.identifier.orcid0000-0002-0303-9416
person.identifier.orcid0000-0001-8429-6977
person.identifier.orcid0000-0002-5264-9755
person.identifier.ridP-6154-2017
person.identifier.ridA-4228-2013
person.identifier.ridE-2102-2014
person.identifier.scopus-author-id57211629814
person.identifier.scopus-author-id55916288400
person.identifier.scopus-author-id6603163260
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication810fc4c7-6c05-44a0-81e5-6cfdb3c2088a
relation.isAuthorOfPublication5c533551-5113-4c0a-93f1-9c50cbd796bc
relation.isAuthorOfPublicatione8577257-c64c-4481-9b2b-940fedb360cc
relation.isAuthorOfPublication.latestForDiscovery5c533551-5113-4c0a-93f1-9c50cbd796bc
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