Immunoinformatics and molecular modeling approaches to design a multi-epitope vaccine against Tibrovirus Congo

Objectives: The objective of this study was to design and evaluate a computationally optimized multi-epitope vaccine targeting the G protein and nucleoprotein of Tibrovirus Congo using immunoinformatics and molecular simulation approaches. Methods: B-cell and T-cell epitopes were predicted, screened for antigenicity, allergenicity, and toxicity, and linked with GPGPG, AAY, and KK linkers. A β-defensin-3 adjuvant and a PADRE sequence were incorporated, generating a 224-amino acid vaccine construct. Population coverage was analyzed globally. The tertiary structure was modeled, refined, and validated using Ramachandran plot analysis and ERRAT scoring. Molecular docking was performed with TLR2 and TLR4 receptors, followed by 100 ns molecular dynamics (MD) simulations. Root mean square deviation (RMSD), root mean square fluctuations (RMSF),radius of gyration, dynamic cross-correlation matrix (DCCM), principal component analysis (PCA), and free energy landscape (FEL) analyses were applied to assess stability and motion. Results: The vaccine construct was predicted to be highly antigenic, non-allergenic, and stable, with 91.81 % global population coverage, highest in Europe and North America. The refined 3D model achieved a Ramachandran favored-region score of 96.3 % and an ERRAT score of 99.63. Docking revealed strong affinity toward TLR2 (-1,665.5, 16 hydrogen bonds) compared to TLR4 (-1,227.8). MD simulations confirmed stable binding, with an average RMSD of 6 Å, a radius of gyration of around 19.8 Å, and RMSF between 1 and 4 Å. DCCM and PCA indicated positive residue correlations and stable conformational dynamics. Immune simulations predicted robust humoral and cellular responses, including elevated IgG, IgM, IL-2, and IFN-γ levels, and long-term memory cell formation. Conclusions: validation is required to confirm safety and effectiveness.