From Algorithm to Antigen: A Mathematical Framework for mRNA Vaccine Design Against Leishmania donovani

This work presents a rational, mathematics-driven framework for computational mRNA vaccine design targeting Leishmania donovani, the causative agent of visceral leishmaniasis. The study introduces the Rami Five-Checkpoint Framework (R5CF), a structured, multi-criteria optimization system that evaluates mRNA vaccine candidates across five critical dimensions: antigen selection, sequence optimization, RNA structural stability, immunogenicity potential, and manufacturability. A multi-epitope mRNA vaccine construct was computationally designed using validated CD8⁺ and CD4⁺ T-cell epitopes derived from L. donovani antigens. The candidate was optimized through GC-constrained codon adaptation and analyzed using the R5CF mathematical scoring model. The final construct achieved balanced performance across checkpoints, demonstrating strong immunogenicity potential, optimized nucleotide composition, and manufacturability feasibility. This work is entirely computational and intended for in-silico research and scientific evaluation. It does not represent clinical validation or therapeutic approval. The results provide a reproducible and quantitative foundation for future computational vaccinology, optimization frameworks, and in-silico vaccine design studies