In silico analysis for the development of multi-epitope vaccines against Mycobacterium tuberculosis

Front Immunol. 2024 Nov 18:15:1474346. doi: 10.3389/fimmu.2024.1474346. eCollection 2024.

Abstract

As Bacille Calmette-Guérin (BCG) vaccine's effectiveness is limited to only children, the development of new tuberculosis (TB) vaccines is being studied using several platforms, and a novel TB vaccine that overcomes this limitation is required. In this study, we designed an effective multi-epitope vaccine against Mycobacterium tuberculosis using immunoinformatic analysis. First, we selected 11 highly antigenic proteins based on previous research: Ag85A, Ag85B, Ag85C, ESAT-6, MPT64, Rv2660c, TB10.4, HspX, GlfT2, Fas, and IniB. Among these antigens, 10 linear B-cell epitopes, 9 helper T-cell epitopes, and 16 cytotoxic T-cell epitopes were predicted to design the multi-epitope vaccine. To improve the immunogenicity of the candidate vaccine, three different adjuvants, griselimycin, human beta-defensin 3 (HBD3), and 50s ribosomal protein (50sRP), were attached with linker sequences to the vaccine model. The immunogenic, antigenic, allergenic, and physicochemical properties of the resulting designed multi-epitope vaccines were predicted in silico. Moreover, 3D structural modeling, refinement, and validation were used to select a model for further evaluation. Molecular docking analysis revealed a consistent and significant binding affinity of the candidate vaccine for toll-like receptors (TLRs), TLR-2, -3, and -4. Immune simulation performed using C-ImmSim demonstrated that three rounds of immunization with multi-epitope vaccines induced a high production of cytokines and immunoglobulins related with both cellular and humoral immune response. Moreover, we constructed vaccine candidate composed of 50sRP and evaluated its immunogenicity in a mouse model. Consequently, this in silico-engineered multi-epitope structure can elicit adaptive immune responses and represents a promising novel candidate for TB vaccine development.

Keywords: adjuvanted vaccine; immunoinformatics analysis; multi-epitope; peptide-based vaccine; tuberculosis (TB).

MeSH terms

  • Animals
  • Antigens, Bacterial* / chemistry
  • Antigens, Bacterial* / immunology
  • Computational Biology / methods
  • Computer Simulation*
  • Epitopes, B-Lymphocyte* / immunology
  • Epitopes, T-Lymphocyte* / immunology
  • Female
  • Humans
  • Immunogenicity, Vaccine
  • Mice
  • Molecular Docking Simulation
  • Mycobacterium tuberculosis* / immunology
  • Tuberculosis Vaccines* / immunology
  • Tuberculosis* / immunology
  • Tuberculosis* / prevention & control
  • Vaccine Development

Substances

  • Tuberculosis Vaccines
  • Epitopes, T-Lymphocyte
  • Antigens, Bacterial
  • Epitopes, B-Lymphocyte

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the Korea National Institute of Health (No. 6600-6634-313 and No. 6600-6634-329), including those for intramural research (No. KNIH 2020-NI-003-00 and KNIH 2022-NI-070-00).