Immunoinformatics-Guided Identification and Functional Screening of T Cell Epitopes from Mycobacterium tuberculosis for Multi-Epitope mRNA Vaccine Design

Background/Objectives: Tuberculosis, caused by Mycobacterium tuberculosis, remains a major global health challenge requiring novel prevention strategies. This study aims to developed an immunoinformatics-guided framework coupled with experimental screening to prioritize for multi-epitope mRNA vaccine design. Methods: Eight immunologically relevant antigens were computationally analyzed to predict cytotoxic (CTL) epitopes and helper T lymphocyte (HTL) epitopes. Population coverage, immune simulation, molecular docking, and normal mode analysis (NMA) were performed in silico. To evaluate peptide immunoreactivity, human IFN-γELISPOT assays were conducted using the candidate peptides, though HLA restriction was not experimentally validated. Results: The workflow identified 14 candidate CTL and 8 HTL epitopes, yielding an estimated global population coverage of 82.6% (60.7% in China; 51.2% in Indonesia). Immune simulations predicted robust humoral and Th1-associated cellular responses, though sustained CD8+ memory responses appeared limited. Docking and NMA suggested favorable structural interactions with TLR3 and TLR4. Crucially, the IFN-γ ELISPOT assay validated eight reactive epitopes that partially coincided with computational predictions within the tested donor group. Conclusions: This study establishes an integrated computational–experimental workflow for T cell epitope prioritization. The identified reactive epitopes provide a preliminary immunological basis and candidate pool for the future design and evaluation of multi-epitope mRNA vaccine strategies against tuberculosis.