Computational Design of a Broad-Spectrum Multi-Epitope mRNA Vaccine Candidate for Mammarenaviruses

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Abstract
Mammarenaviruses cause serious diseases such as Lassa fever and Argentine hemorrhagic fever that threaten global public health; however, vaccines and therapeutic options remain limited. Therefore, developing a broadly effective vaccine against mammarenaviruses is a priority. In this study, we systematically analyzed the main structural protein sequences of ten mammarenaviruses. We combined antigenic epitopes from the Immune Epitope Database (IEDB) and used inverse vaccination and immunoinformatics methods to screen for highly conserved B- and T-cell epitopes with strong immunogenicity. We then added tissue plasminogen activator (tPA), which is the most effective vaccine against these pathogens. To construct a broad-spectrum multi-epitope fusion mRNA vaccine candidate, we included a tPA signal peptide, PADRE adjuvant, and linkers. The mRNA sequences were algorithm-optimized. We assessed the structural stability and immunogenicity of the candidate vaccine using molecular docking, molecular dynamics (MD) simulation, and immunosimulation. The designed vaccine had good antigenicity and structural stability, forming stable complexes with a variety of intrinsic immunoreceptors and triggering a strong, sustained, and comprehensive immune response during immunosimulation. Our study findings posit the designed vaccine as a potential broad-spectrum multi-epitope vaccine candidate against mammarenaviruses. Further real-world studies are required to validate these results.

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