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

Li Zhuang; Awais Ali; Ling Yang; Zhaoyang Ye; Linsheng Li; Ruizi Ni; Yajing An; Syed Luqman Ali; Wenping Gong

doi:10.1016/j.imj.2024.100148

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

Infect Med (Beijing). 2024 Nov 9;3(4):100148. doi: 10.1016/j.imj.2024.100148. eCollection 2024 Dec.

Authors

Li Zhuang^{1

2}, Awais Ali³, Ling Yang^{1

2}, Zhaoyang Ye^{1

2}, Linsheng Li^{1

2}, Ruizi Ni^{1

2}, Yajing An^{1

2}, Syed Luqman Ali³, Wenping Gong^{1

2}

Affiliations

¹ Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The Eighth Medical Center of PLA General Hospital, Beijing 100091, China.
² Graduate School, Hebei North University, Zhangjiakou 075000, Hebei Province, China.
³ Department of Biochemistry, Abdul Wali Khan University, Mardan 23200, Pakistan.

Abstract

Background: 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.

Methods: 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 (MD), and immunological characteristics of the MEV.

Results: We constructed a MEV, designated ZL12138L, containing 13 helper T lymphocyte epitopes, 12 cytotoxic T lymphocyte epitopes, 8 B-cell epitopes, as well as Toll-like receptor (TLR) agonists and helper peptides. Bioinformatics analyses revealed that ZL12138L should exhibit excellent immunogenicity and antigenicity, with no toxicity or allergenicity, and had potential to induce robust immune responses and high solubility, the immunogenicity score was 4.14449, the antigenicity score was 0.8843, and the immunological score was 0.470. Moreover, ZL12138L showed high population coverage for human leukocyte antigen class I and II alleles, reaching 92.41% and 90.17%, respectively, globally. Molecular docking analysis indicated favorable binding affinity of ZL12138L with TLR-2 and TLR-4, with binding energies of -1173.4 and -1360.5 kcal/mol, respectively. Normal mode analysis and MD simulations indicated the stability and dynamic properties of the vaccine construct. Immune simulation predictions suggested that ZL12138L could effectively activate innate and adaptive immune cells, inducing high levels of Type 1 T helper cell cytokines.

Conclusions: This study provides compelling evidence for ZL12138L as a promising TB vaccine candidate. Future research will focus on experimental validation and further optimization of the vaccine design.

Keywords: Artificial intelligence; Computer-aided design; Immunogenicity; Multi-epitope vaccine; Tuberculosis.