An Immunoinformatics Approach for Designing a Novel Multi‐Epitope Vaccine Against Junín Virus

ABSTRACT The absence of a specific treatment or vaccine is why preventive measures and public health education should be prioritized to avoid infection with the lethal Junín virus. The study aims to create a chimeric peptide vaccine against the viral glycoprotein (GP) to offer a solution for immunotherapy against the disease. We employed in silico immunoinformatics tools, and we resorted to several computer models to predict the most frequent major histocompatibility complex (MHC) binding complexes and the B‐cell‐epitope that may enhance the host immune response. After docking the top epitopes with the vaccine candidate, MD simulations were performed to validate the design. The virtual cloning and codon optimization calculations were also assessed to validate the expression potential of the suggested vaccine model in the bacterial host. The selected epitopes demonstrated high antigenicity while showing no allergenic or toxic effects. It also exhibited satisfactory stability with the cellular epitopes, and its expression potential was validated. In conclusion, this study presents a novel multiepitope‐based vaccine construct as a potential immunization candidate against Junín virus infection. The designed synthetic construct demonstrates promising attributes for the development of an effective vaccine model. Its prospective efficacy represents an encouraging advancement in the ongoing efforts to combat Junín virus‐associated disease.