In Silico Prioritization of a Hantavirus Glycoprotein-Derived Multi-Epitope mRNA Vaccine Candidate Using the Rami Five-Checkpoint Framework

This preprint presents a purely in-silico computational study evaluating the Hantavirus CGRn8316 glycoprotein (GenBank accession ABS83832.1) as a potential mRNA vaccine antigen. I applied the Rami Five-Checkpoint Framework to compare the full-length glycoprotein with a shorter trimmed multi-epitope construct. The framework scored five design areas: antigen selection, sequence optimization, RNA design feasibility, epitope-like immunogenicity ranking, and cost/scalability.

The full-length glycoprotein was analyzed for protein size, molecular weight, GC content, hydropathy, cysteine burden, glycosylation motifs, and transmembrane-like regions. A shorter 82-amino-acid multi-epitope construct was then designed by selecting seven high-scoring epitope-like regions and joining them with AAY linkers. This reduced the mRNA size from 3,399 nucleotides to 246 nucleotides, eliminated predicted transmembrane-like regions, and improved the overall framework score from 0.728 to 0.820. After conceptual in-silico causal validation, the final validated score for the trimmed construct was 0.833. 

The purpose of this work is to demonstrate a transparent, mathematical, and reproducible method for early-stage vaccine candidate prioritization. This study is hypothesis-generating only. No wet-laboratory experiments, infectious virus handling, animal testing, clinical testing, or therapeutic validation were performed. The proposed construct requires independent validation using accepted immunological, structural, safety, and preclinical assays before any real-world application.