Clinical Serum-Anchored Computational Design Pipeline for a Broad-Spectrum Influenza Multi-Epitope mRNA Vaccine

Area of Science:

• Vaccinology
• Computational Biology
• Immunology

Background:

• Influenza viruses pose a pandemic threat due to rapid antigenic drift, reducing conventional vaccine effectiveness.
• Developing broadly protective vaccines against influenza remains a significant challenge.

Purpose of the Study:

• To establish a clinical serum-anchored computational design pipeline for broad-spectrum multi-epitope mRNA vaccines (MEMVs).
• To bridge the gap between in silico vaccine design and clinical application for influenza.

Main Methods:

• Utilized longitudinal human sera and antibody-peptide microarrays to identify 12 immunodominant B-cell linear epitopes from influenza A and B nucleoprotein.
• Combined experimentally validated epitopes with in silico-predicted conserved T-cell epitopes (NP/HA/NA) to construct MEMV candidates.
• Employed molecular docking, molecular dynamics simulations, and in silico immunization (C-ImmSim) to assess MEMV-TLR3 compatibility and predict immune responses.

Main Results:

• Identified 12 immunodominant B-cell linear epitopes from influenza nucleoprotein.
• Constructed MEMV candidates with high antigenicity, non-toxicity, and 95.63% global HLA coverage.
• In silico simulations predicted robust B/T-cell responses and protective cytokine production (IFN-γ/IL-10).

Conclusions:

• The developed pipeline significantly shortens the preliminary design cycle for influenza vaccines.
• This approach offers a standardized strategy for combining epitopes for vaccine development.
• The identified epitopes serve as direct targets for future in vitro and in vivo studies.