A Machine Learning-Guided Approach for a Multi-Epitope HIV Vaccine Design - Bento, Pedro; Aquino, Yan; Buzelin, Arthur; Rigueira, Pedro B.; Gambogi, André; Porfírio, Luisa G.; Doria, Italo; Anunciação, Sofia; Mendes, Gabriel; Minardi, Raquel; Paim, Adriana Alves; Pappa, Gisele L.; et al. (Revista Eletrônica de Iniciação Científica em Computação, 2025)
Developing an effective HIV vaccine remains challenging due to the virus's variability and complex immune responses. We propose a novel multi-epitope vaccine design using machine learning and computational methods to identify conserved, immunodominant epitopes from diverse HIV variants. These epitopes, selected to elicit humoral and cellular responses -- targeting CD4+ T cells, CD8+ cytotoxic T cells, and B cells -- are incorporated into a chimeric protein delivered via a viral vector to enhance immunity. Our framework integrates epitope selection, in silico physicochemical predictions, 3D construction of the chimeric protein, illustrated in Figure 1, and in vitro analysis, contributing to the development of a broadly protective and durable HIV vaccine.