Computational epitope profiling and AI-driven protein engineering enable rational design of multi-epitope vaccines against Mycobacterium tuberculosis

作者：Xinfeng Li, Xinyu Tao, Mingyue Zhong, Yiyao Wang, Heng Xue, Binda T. Andongma, Shan‐Ho Chou, Hongping Wei, Jin He, Hang Yang · 发表于：Computational and Structural Biotechnology Journal · 年份：2025 · DOI：10.1016/j.csbj.2025.09.015 · 被引用次数：4 · 研究领域：vaccines and immunoinformatics approaches、Monoclonal and Polyclonal Antibodies Research、Bacteriophages and microbial interactions

(Mtb), remains a major global health threat, accounting for approximately 1.5 million deaths annually. The rise of antibiotic-resistant strains further complicates treatment efforts. While vaccination is a cornerstone of disease control, the only licensed TB vaccine, Bacille Calmette-Guérin (BCG), shows limited efficacy in adults. There is thus a critical need for more effective vaccines. Multi-epitope vaccines, which incorporate key epitopes from multiple antigens, offer a promising strategy by eliciting both humoral and cellular immunity. Here, we employed a comparative epitopomics approach to identify immunodominant epitopes from eight major Mtb antigens and selected 17 potent epitopes for the design of a multi-epitope antigen. Using AI-driven protein design, we systematically optimized epitope arrangement and flanking sequences to generate a stable, structurally integrated antigen-MtbEpi-17. Computational analyses suggest that MtbEpi-17 can effectively interact with TLR2 and TLR4, potentially stimulating robust innate and adaptive immune responses. Our study provides a rational design framework for multi-epitope vaccines, and proposes MtbEpi-17 as a strong candidate for further preclinical and clinical evaluation.