Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests

Detalhes bibliográficos
Autor(a) principal: Castro, Ana C. H.
Data de Publicação: 2022
Outros Autores: Bezerra, Ítalo R. S., Pascon, Aline M., Da Silva, Gabriela H., Philot, Eric A., De Oliveira, Vivian L., Mancini, Rodrigo S. N., Schleder, Gabriel R., Castro, Carlos E., De Carvalho, Luciani R. S., Fernandes, Bianca H. V., Cilli, Eduardo M. [UNESP], Sanches, Paulo R. S. [UNESP], Santhiago, Murilo, Charlie-Silva, Ives, Martinez, Diego S. T., Scott, Ana L., Alves, Wendel A., Lima, Renato S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1021/acsnano.2c04364
http://hdl.handle.net/11449/241701
Resumo: Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.
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spelling Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Testselectrochemical impedance spectroscopygold nanoparticlemachine learningnoncovalent immobilizationSARS-CoV-2Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.Brazilian Nanotechnology National Laboratory Brazilian Center for Research in Energy and Materials, CampinasCenter for Natural and Human Sciences Federal University of ABC, Santo AndréInstitute of Biomedical Sciences University of São Paulo São PauloInstitute of Chemistry University of Campinas, CampinasSão Carlos Institute of Chemistry University of São Paulo, São CarlosLaboratory of Immunology Heart Institute University of São Paulo São PauloCenter for Mathematics Computing and Cognition Federal University of ABC, Santo AndréMedical School University of Sao Paulo São PauloInstitute of Chemistry São Paulo State University, AraraquaraJohn A. Paulson School of Engineering and Applied Sciences Harvard UniversityInstitute of Chemistry São Paulo State University, AraraquaraBrazilian Center for Research in Energy and MaterialsFederal University of ABCUniversidade de São Paulo (USP)Universidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Harvard UniversityCastro, Ana C. H.Bezerra, Ítalo R. S.Pascon, Aline M.Da Silva, Gabriela H.Philot, Eric A.De Oliveira, Vivian L.Mancini, Rodrigo S. N.Schleder, Gabriel R.Castro, Carlos E.De Carvalho, Luciani R. S.Fernandes, Bianca H. V.Cilli, Eduardo M. [UNESP]Sanches, Paulo R. S. [UNESP]Santhiago, MuriloCharlie-Silva, IvesMartinez, Diego S. T.Scott, Ana L.Alves, Wendel A.Lima, Renato S.2023-03-01T21:17:30Z2023-03-01T21:17:30Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1021/acsnano.2c04364ACS Nano.1936-086X1936-0851http://hdl.handle.net/11449/24170110.1021/acsnano.2c043642-s2.0-85136624110Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengACS Nanoinfo:eu-repo/semantics/openAccess2023-03-01T21:17:31Zoai:repositorio.unesp.br:11449/241701Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:31:11.683090Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
title Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
spellingShingle Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
Castro, Ana C. H.
electrochemical impedance spectroscopy
gold nanoparticle
machine learning
noncovalent immobilization
SARS-CoV-2
title_short Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
title_full Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
title_fullStr Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
title_full_unstemmed Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
title_sort Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests
author Castro, Ana C. H.
author_facet Castro, Ana C. H.
Bezerra, Ítalo R. S.
Pascon, Aline M.
Da Silva, Gabriela H.
Philot, Eric A.
De Oliveira, Vivian L.
Mancini, Rodrigo S. N.
Schleder, Gabriel R.
Castro, Carlos E.
De Carvalho, Luciani R. S.
Fernandes, Bianca H. V.
Cilli, Eduardo M. [UNESP]
Sanches, Paulo R. S. [UNESP]
Santhiago, Murilo
Charlie-Silva, Ives
Martinez, Diego S. T.
Scott, Ana L.
Alves, Wendel A.
Lima, Renato S.
author_role author
author2 Bezerra, Ítalo R. S.
Pascon, Aline M.
Da Silva, Gabriela H.
Philot, Eric A.
De Oliveira, Vivian L.
Mancini, Rodrigo S. N.
Schleder, Gabriel R.
Castro, Carlos E.
De Carvalho, Luciani R. S.
Fernandes, Bianca H. V.
Cilli, Eduardo M. [UNESP]
Sanches, Paulo R. S. [UNESP]
Santhiago, Murilo
Charlie-Silva, Ives
Martinez, Diego S. T.
Scott, Ana L.
Alves, Wendel A.
Lima, Renato S.
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Brazilian Center for Research in Energy and Materials
Federal University of ABC
Universidade de São Paulo (USP)
Universidade Estadual de Campinas (UNICAMP)
Universidade Estadual Paulista (UNESP)
Harvard University
dc.contributor.author.fl_str_mv Castro, Ana C. H.
Bezerra, Ítalo R. S.
Pascon, Aline M.
Da Silva, Gabriela H.
Philot, Eric A.
De Oliveira, Vivian L.
Mancini, Rodrigo S. N.
Schleder, Gabriel R.
Castro, Carlos E.
De Carvalho, Luciani R. S.
Fernandes, Bianca H. V.
Cilli, Eduardo M. [UNESP]
Sanches, Paulo R. S. [UNESP]
Santhiago, Murilo
Charlie-Silva, Ives
Martinez, Diego S. T.
Scott, Ana L.
Alves, Wendel A.
Lima, Renato S.
dc.subject.por.fl_str_mv electrochemical impedance spectroscopy
gold nanoparticle
machine learning
noncovalent immobilization
SARS-CoV-2
topic electrochemical impedance spectroscopy
gold nanoparticle
machine learning
noncovalent immobilization
SARS-CoV-2
description Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (AbS) by the S protein binding site, we synthesized the peptide sequence of Asn-Asn-Ala-Thr-Asn-COOH (abbreviated as PEP2003) to create COVID-19 screening label-free (LF) biosensors based on a carbon electrode, gold nanoparticles (AuNPs), and electrochemical impedance spectroscopy. The PEP2003 is easily obtained by chemical synthesis, and it can be adsorbed on electrodes while maintaining its ability for AbS recognition, further leading to a sensitivity 3.4-fold higher than the full-length S protein, which is in agreement with the increase in the target-to-receptor size ratio. Peptide-loaded LF devices based on noncovalent immobilization were developed by affording fast and simple analyses, along with a modular functionalization. From studies by molecular docking, the peptide-AbS binding was found to be driven by hydrogen bonds and hydrophobic interactions. Moreover, the peptide is not amenable to denaturation, thus addressing the trade-off between scalability, cost, and robustness. The biosensor preserves 95.1% of the initial signal for 20 days when stored dry at 4 °C. With the aid of two simple equations fitted by machine learning (ML), the method was able to make the COVID-19 screening of 39 biological samples into healthy and infected groups with 100.0% accuracy. By taking advantage of peptide-related merits combined with advances in surface chemistry and ML-aided accuracy, this platform is promising to bring COVID-19 biosensors into mainstream use toward straightforward, fast, and accurate analyses at the point of care, with social and economic impacts being achieved.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-03-01T21:17:30Z
2023-03-01T21:17:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1021/acsnano.2c04364
ACS Nano.
1936-086X
1936-0851
http://hdl.handle.net/11449/241701
10.1021/acsnano.2c04364
2-s2.0-85136624110
url http://dx.doi.org/10.1021/acsnano.2c04364
http://hdl.handle.net/11449/241701
identifier_str_mv ACS Nano.
1936-086X
1936-0851
10.1021/acsnano.2c04364
2-s2.0-85136624110
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ACS Nano
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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