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Castro ACH, Bezerra ÍRS, Pascon AM, da Silva GH, Philot EA, de Oliveira VL, Mancini RSN, Schleder GR, Castro CE, de Carvalho LRS, Fernandes BHV, Cilli EM, Sanches PRS, Santhiago M, Charlie-Silva I, Martinez DST, Scott AL, Alves WA, Lima RS. Modular Label-Free Electrochemical Biosensor Loading Nature-Inspired Peptide toward the Widespread Use of COVID-19 Antibody Tests. ACS NANO 2022; 16:14239-14253. [PMID: 35969505 PMCID: PMC9397565 DOI: 10.1021/acsnano.2c04364] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/11/2022] [Indexed: 05/16/2023]
Abstract
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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Affiliation(s)
- Ana C. H. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ítalo R. S. Bezerra
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Aline M. Pascon
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriela H. da Silva
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Eric A. Philot
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Vivian L. de Oliveira
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Laboratory of Immunology, Heart Institute,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Rodrigo S. N. Mancini
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Gabriel R. Schleder
- John A. Paulson School of Engineering and Applied
Sciences, Harvard University, Cambridge, Massachusetts 02138,
United States
| | - Carlos E. Castro
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | | | | | - Eduardo M. Cilli
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Paulo R. S. Sanches
- Institute of Chemistry, São Paulo
State University, Araraquara, São Paulo 14800-900,
Brazil
| | - Murilo Santhiago
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Ives Charlie-Silva
- Institute of Biomedical Sciences,
University of São Paulo, São Paulo, São
Paulo 05508-000, Brazil
| | - Diego S. T. Martinez
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
| | - Ana L. Scott
- Center for Mathematics, Computing and Cognition,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Wendel A. Alves
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
| | - Renato S. Lima
- Brazilian Nanotechnology National Laboratory,
Brazilian Center for Research in Energy and Materials,
Campinas, São Paulo 13083-970, Brazil
- Center for Natural and Human Sciences,
Federal University of ABC, Santo André, São
Paulo 09210-580, Brazil
- Institute of Chemistry, University of
Campinas, Campinas, São Paulo 13083-970,
Brazil
- São Carlos Institute of Chemistry,
University of São Paulo, São Carlos, São
Paulo 09210-580, Brazil
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