Leveraging computer-aided design and artificial intelligence to develop a next-generation multi-epitope tuberculosis vaccine candidate

作者：Zhuang Li, Awais Ali, Ling Yang, Zhaoyang Ye, Linsheng Li, Ruizi Ni, Yajing An, Syed Luqman Ali, Wenping Gong · 发表于：Infectious Medicine · 年份：2024 · DOI：10.1016/j.imj.2024.100148 · 被引用次数：25 · 研究领域：vaccines and immunoinformatics approaches、Tuberculosis Research and Epidemiology、Immune responses and vaccinations

• The design and analysis of a novel multi-epitope vaccine (MEV) named ZL12138L against Mycobacterium tuberculosis (MTB) using bioinformatics and immunoinformatics approaches. • The identification of immunodominant epitopes from MTB antigens with broad population coverage, predicted to cover HLA class I and II allele genes in 92.41% and 90.17% of the global population, respectively. • The demonstration of the ZL12138L vaccine's significant immunogenicity and antigenicity, with no toxicity or allergenicity, through comprehensive bioinformatics analysis. • The successful molecular docking of the ZL12138L vaccine with Toll-like receptor (TLR) 2 and TLR4, indicating its potential to activate both innate and adaptive immune responses. • The prediction of robust immune responses, including the activation of NK cells, macrophages, B lymphocytes, and T lymphocytes, following immunization with the ZL12138L vaccine. Tuberculosis (TB) remains a global public health challenge. The existing Bacillus Calmette–Guérin vaccine has limited efficacy in preventing adult pulmonary TB, necessitating the development of new vaccines with improved protective effects. Computer-aided design and artificial intelligence technologies, combined with bioinformatics and immunoinformatics approaches, were used to design a multi-epitope vaccine (MEV) against TB. Comprehensive bioinformatics analyses were conducted to evaluate the physicochemical properties, spatial structure, immunogenicity, molecular dynamics...