SciNova Research Press

This preprint presents a purely in-silico computational study evaluating the Hantavirus CGRn8316 glycoprotein as a potential mRNA vaccine antigen. The Rami Five-Checkpoint Framework was applied to compare the full-length glycoprotein with a shorter trimmed multi-epitope construct, scoring antigen selection, sequence optimization, RNA design feasibility, epitope-like immunogenicity, and cost/scalability.

This work presents the computational design of a multi-epitope mRNA vaccine candidate against Zaire ebolavirus using an immunoinformatics-driven approach. Conserved regions from the Ebola glycoprotein (GP), nucleoprotein (NP), and matrix protein VP40 were analyzed to identify high-affinity CD8+ and CD4+ T-cell epitopes.

This book presents a unified conceptual and computational framework that envisions Artificial Intelligence (AI) as an extension of humanity's adaptive immune system. Through advanced in-silico modeling, the work demonstrates how machine learning and bioinformatics pipelines can emulate immune recognition, predict antigenic targets, and generate mRNA-based vaccine and tolerance candidates.