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

• Autor(a) principal: Garcia-Junior, Marcelo Augusto
• Data de Publicação: 2025
• Outros Autores: Andrade, Bruno Silva, Lima, Ana Paula, Soares, Iara Pereira, Notário, Ana Flávia Oliveira, Bernardino, Sttephany Silva, Guevara-Vega, Marco Fidel, Honório-Silva, Ghabriel, Munoz, Rodrigo Alejandro Abarza, Jardim, Ana Carolina Gomes [UNESP], Martins, Mário Machado, Goulart, Luiz Ricardo, Cunha, Thulio Marquez, Carneiro, Murillo Guimarães, Sabino-Silva, Robinson
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/bios15020075; https://hdl.handle.net/11449/301094
• topic: artificial intelligence; bio-inspired peptides; biosensors; COVID-19; electrochemical detection; salivary diagnostics

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)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.