Artificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms

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Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.contributorState University of Southwest of Bahia (UESB)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorGarcia-Junior, Marcelo Augusto-
Autor(es): dc.creatorAndrade, Bruno Silva-
Autor(es): dc.creatorLima, Ana Paula-
Autor(es): dc.creatorSoares, Iara Pereira-
Autor(es): dc.creatorNotário, Ana Flávia Oliveira-
Autor(es): dc.creatorBernardino, Sttephany Silva-
Autor(es): dc.creatorGuevara-Vega, Marco Fidel-
Autor(es): dc.creatorHonório-Silva, Ghabriel-
Autor(es): dc.creatorMunoz, Rodrigo Alejandro Abarza-
Autor(es): dc.creatorJardim, Ana Carolina Gomes-
Autor(es): dc.creatorMartins, Mário Machado-
Autor(es): dc.creatorGoulart, Luiz Ricardo-
Autor(es): dc.creatorCunha, Thulio Marquez-
Autor(es): dc.creatorCarneiro, Murillo Guimarães-
Autor(es): dc.creatorSabino-Silva, Robinson-
Data de aceite: dc.date.accessioned2025-08-21T17:35:57Z-
Data de disponibilização: dc.date.available2025-08-21T17:35:57Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/bios15020075-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301094-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301094-
Descrição: dc.descriptionDeveloping 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)6]3−/4 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.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment 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-
Descrição: dc.descriptionDepartment of Biological Sciences Laboratory of Bioinformatics and Computational Chemistry State University of Southwest of Bahia (UESB)-
Descrição: dc.descriptionPost-Graduation Program in Genetics and Biochemistry Laboratory of Nanobiotechnology—Dr Luiz Ricardo Goulart Federal University of Uberlândia (UFU), Uberlâ, ndia-
Descrição: dc.descriptionInstitute of Chemistry Federal University of Uberlândia (UFU)-
Descrição: dc.descriptionInstitute of Biosciences Languages and Exact Sciences (Ibilce) São Paulo State University (Unesp)-
Descrição: dc.descriptionLaboratory of Antiviral Research Department of Microbiology Institute of Biomedical Sciences Federal University of Uberlandia (UFU), Uberlândia 38408-100-
Descrição: dc.descriptionDepartment of Pulmonology School of Medicine Federal University of Uberlandia (UFU)-
Descrição: dc.descriptionFaculty of Computing Federal University of Uberlandia (UFU)-
Descrição: dc.descriptionInstitute of Biosciences Languages and Exact Sciences (Ibilce) São Paulo State University (Unesp)-
Descrição: dc.descriptionCNPq: #406840/2022-9-
Descrição: dc.descriptionCNPq: #465669/2014-0-
Idioma: dc.languageen-
Relação: dc.relationBiosensors-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial intelligence-
Palavras-chave: dc.subjectbio-inspired peptides-
Palavras-chave: dc.subjectbiosensors-
Palavras-chave: dc.subjectCOVID-19-
Palavras-chave: dc.subjectelectrochemical detection-
Palavras-chave: dc.subjectsalivary diagnostics-
Título: dc.titleArtificial-Intelligence Bio-Inspired Peptide for Salivary Detection of SARS-CoV-2 in Electrochemical Biosensor Integrated with Machine Learning Algorithms-
Tipo de arquivo: dc.typelivro digital-
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