Recombinant Immunogens Designed by AI Epitope Prioritization Confer Protection Against Mycobacterium tuberculosis

Background: Tuberculosis (TB) remains a major global health challenge, highlighting the urgent need for more effective vaccines. This study aimed to develop an artificial intelligence-guided epitope prediction and prioritization pipeline to identify immunodominant peptides from Mycobacterium tuberculosis (Mtb) and to evaluate the immunogenicity and protective efficacy of the resulting vaccine candidates. Methods: An AI-guided framework was used to predict and prioritize immunodominant Mtb epitopes, leading to the generation of 72 recombinant immunogens. Among these, RI-13, RI-20, and RI-31 were selected as the leading candidates. Their protective efficacy was assessed in a zebrafish TB infection model and in BALB/c mice following DNA vaccination. Humoral and cellular immune responses were further evaluated in C57BL/6 mice. Results: RI-13, RI-20, and RI-31 markedly reduced infection-associated pathology and lowered bacterial burden by up to 1.5 log₁₀ in the zebrafish TB infection model, outperforming benchmark antigen combinations, including the Ag85A plus ESAT6/CFP10 cocktail used in the phase III vaccine candidate GamTBvac. In BALB/c mice, DNA vaccination with each construct reduced pulmonary mycobacterial burden by approximately 0.3 log₁₀ and alleviated lung tissue damage. In addition, all three candidates elicited robust humoral and cellular immune responses, with RI-13 showing the strongest overall immunogenicity and inducing a balanced Th1, Th2, and Th17 response profile in C57BL/6 mice. Conclusions: These findings identify RI-13, derived from Rv1174c, as a promising next-generation TB vaccine candidate. More broadly, this study supports the utility of an AI-guided framework for the rational design and preclinical prioritization of novel TB immunogens.