Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms
Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPE...
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MDPI AG
2025-01-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/15/2/75 |
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| author | Marcelo Augusto Garcia-Junior Bruno Silva Andrade Ana Paula Lima Iara Pereira Soares Ana Flávia Oliveira Notário Sttephany Silva Bernardino Marco Fidel Guevara-Vega Ghabriel Honório-Silva Rodrigo Alejandro Abarza Munoz Ana Carolina Gomes Jardim Mário Machado Martins Luiz Ricardo Goulart Thulio Marquez Cunha Murillo Guimarães Carneiro Robinson Sabino-Silva |
| author_facet | Marcelo Augusto Garcia-Junior Bruno Silva Andrade Ana Paula Lima Iara Pereira Soares Ana Flávia Oliveira Notário Sttephany Silva Bernardino Marco Fidel Guevara-Vega Ghabriel Honório-Silva Rodrigo Alejandro Abarza Munoz Ana Carolina Gomes Jardim Mário Machado Martins Luiz Ricardo Goulart Thulio Marquez Cunha Murillo Guimarães Carneiro Robinson Sabino-Silva |
| author_sort | Marcelo Augusto Garcia-Junior |
| collection | DOAJ |
| description | Developing affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein’s RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)<sub>6</sub>]<sup>3−/4</sup> solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of −250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva. |
| format | Article |
| id | doaj-art-4d436bfbe97b4ed6bbf78aed6ee4376a |
| institution | DOAJ |
| issn | 2079-6374 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj-art-4d436bfbe97b4ed6bbf78aed6ee4376a2025-08-20T02:44:52ZengMDPI AGBiosensors2079-63742025-01-011527510.3390/bios15020075Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning AlgorithmsMarcelo Augusto Garcia-Junior0Bruno Silva Andrade1Ana Paula Lima2Iara Pereira Soares3Ana Flávia Oliveira Notário4Sttephany Silva Bernardino5Marco Fidel Guevara-Vega6Ghabriel Honório-Silva7Rodrigo Alejandro Abarza Munoz8Ana Carolina Gomes Jardim9Mário Machado Martins10Luiz Ricardo Goulart11Thulio Marquez Cunha12Murillo Guimarães Carneiro13Robinson Sabino-Silva14Department of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Biological Sciences, Laboratory of Bioinformatics and Computational Chemistry, State University of Southwest of Bahia (UESB), Jequié 45205-490, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilPost-Graduation Program in Genetics and Biochemistry, Laboratory of Nanobiotechnology—Dr Luiz Ricardo Goulart, Federal University of Uberlândia (UFU), Uberlândia 38408-100, BrazilPost-Graduation Program in Genetics and Biochemistry, Laboratory of Nanobiotechnology—Dr Luiz Ricardo Goulart, Federal University of Uberlândia (UFU), Uberlândia 38408-100, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilInstitute of Chemistry, Federal University of Uberlândia (UFU), Uberlândia 38408-100, BrazilInstitute of Biosciences, Languages, and Exact Sciences (Ibilce), São Paulo State University (Unesp), São José do Rio Preto 15054-000, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Pulmonology, School of Medicine, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilFaculty of Computing, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDepartment of Physiology, Laboratory of Nanobiotechnology—Dr. Luiz Ricardo Goulart, Innovation Center in Salivary Diagnostic and Nanobiotechnology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlândia 38408-100, BrazilDeveloping affordable, rapid, and accurate biosensors is essential for SARS-CoV-2 surveillance and early detection. We created a bio-inspired peptide, using the SAGAPEP AI platform, for COVID-19 salivary diagnostics via a portable electrochemical device coupled to Machine Learning algorithms. SAGAPEP enabled molecular docking simulations against the SARS-CoV-2 Spike protein’s RBD, leading to the synthesis of Bio-Inspired Artificial Intelligence Peptide 1 (BIAI1). Molecular docking was used to confirm interactions between BIAI1 and SARS-CoV-2, and BIAI1 was functionalized on rhodamine-modified electrodes. Cyclic voltammetry (CV) using a [Fe(CN)<sub>6</sub>]<sup>3−/4</sup> solution detected virus levels in saliva samples with and without SARS-CoV-2. Support vector machine (SVM)-based machine learning analyzed electrochemical data, enhancing sensitivity and specificity. Molecular docking revealed stable hydrogen bonds and electrostatic interactions with RBD, showing an average affinity of −250 kcal/mol. Our biosensor achieved 100% sensitivity, 80% specificity, and 90% accuracy for 1.8 × 10⁴ focus-forming units in infected saliva. Validation with COVID-19-positive and -negative samples using a neural network showed 90% sensitivity, specificity, and accuracy. This BIAI1-based electrochemical biosensor, integrated with machine learning, demonstrates a promising non-invasive, portable solution for COVID-19 screening and detection in saliva.https://www.mdpi.com/2079-6374/15/2/75biosensorsCOVID-19bio-inspired peptidesartificial intelligencesalivary diagnosticselectrochemical detection |
| spellingShingle | Marcelo Augusto Garcia-Junior Bruno Silva Andrade Ana Paula Lima Iara Pereira Soares Ana Flávia Oliveira Notário Sttephany Silva Bernardino Marco Fidel Guevara-Vega Ghabriel Honório-Silva Rodrigo Alejandro Abarza Munoz Ana Carolina Gomes Jardim Mário Machado Martins Luiz Ricardo Goulart Thulio Marquez Cunha Murillo Guimarães Carneiro Robinson Sabino-Silva Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms Biosensors biosensors COVID-19 bio-inspired peptides artificial intelligence salivary diagnostics electrochemical detection |
| title | Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms |
| title_full | Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms |
| title_fullStr | Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms |
| title_full_unstemmed | Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms |
| title_short | Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms |
| title_sort | artificial intelligence bio inspired peptide for salivary detection of sars cov 2 in electrochemical biosensor integrated with machine learning algorithms |
| topic | biosensors COVID-19 bio-inspired peptides artificial intelligence salivary diagnostics electrochemical detection |
| url | https://www.mdpi.com/2079-6374/15/2/75 |
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