In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis

Andongma, Binda T.; Huang, Yazheng; Chen, Fang; Tang, Qing; Yang, Min; Chou, Shan‐Ho; Li, Xinfeng; He, Jin

doi:10.1016/j.csbj.2023.01.019

Cited by 20 publications

(9 citation statements)

References 141 publications

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“…Recent advances in bioinformatics, structural biology, and immunoinformatics, coupled with the growing use of various artificial intelligence algorithms and modeling tools, have dramatically altered traditional vaccine design strategies, enabling a more precise and efficient design of vaccine molecules [56,57]. In light of these advances, several studies have focused on designing prophylactic or therapeutic vaccines to combat TB infection [58][59][60][61][62][63][64]. However, most researchers have focused on developing new vaccines against active TB and have neglected the development of vaccines against LTBI.…”

Section: Discussionmentioning

confidence: 99%

Design and development of a multi‐epitope vaccine for the prevention of latent tuberculosis infection

Jiang,

Wang,

Wang

et al. 2023

Medicine Advances

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BackgroundLatent tuberculosis infection (LTBI) often progresses to active tuberculosis, necessitating the development of novel vaccine to prevent LTBI. In this study, we aimed to design a Mycobacterium tuberculosis (M. tuberculosis) vaccine that could elicit a potent immune response to prevent LTBI.MethodsWe used bioinformatics and immunoinformatics techniques to develop a multi‐epitope vaccine (MEV) called C624P. The vaccine contained six cytotoxic T lymphocytes (CTL), two helper T lymphocytes (HTL), and four B‐cell epitopes derived from six antigens associated with LTBI and the Mycobacterium tuberculosis region of difference. We added Toll‐like receptor (TLR) agonists and PADRE peptide to the MEV to enhance its immunogenicity. We then analyzed the C624P vaccine's physical and chemical properties, spatial structure, molecular docking with TLRs, and immunological features.ResultsThe C624P vaccine displayed good antigenicity and immunogenicity scores of 0.901398 and 3.65609, respectively. The vaccine structure was stable, with 42.82% α‐helix content, a Z‐value of −7.84, and a favored Ramachandran plot area of 85.84% after majorization. Molecular docking analysis showed that the C624P vaccine could bind tightly to TLR2 (−1011.0 kcal/mol) and TLR4 (−941.4 kcal/mol). Furthermore, the C624P vaccine effectively stimulated T and B lymphocytes, resulting in high levels of Th1 cytokines such as IFN‐γ and IL‐2.ConclusionsThe C624P vaccine represents a promising MEV for preventing LTBI. The vaccine's good antigenicity, immunogenicity, stability, and ability to activate immune responses suggest its effectiveness in preventing LTBI. Our study demonstrated the utility of bioinformatics and immunoinformatics techniques in designing safe and effective tuberculosis vaccines.

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Section: Discussionmentioning

confidence: 99%

Design and development of a multi‐epitope vaccine for the prevention of latent tuberculosis infection

Jiang,

Wang,

Wang

et al. 2023

Medicine Advances

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“…A potential vaccine against SARS-CoV-2 16 , 22 , 24 – 26 , malaria 27 , 28 , Ebola virus 29 , dengue virus field 30 , hepatitis B virus field 31 , Staphylococcus aureus 32 , 33 , Acinetobacter baumannii 34 – 36 , and Helicobacter pylori 37 has been developed using a variety of immuno-informatics techniques. Recently, numerous immunoinformatic tools such as 2 , 15 , 24 , 38 , 39 have been utilized in the design of TB epitope-based vaccines.…”

Section: Related Workmentioning

confidence: 99%

“…These epitopes were then conjugated with adjuvant and PADRE, and using the proper linkers. Although the proposed vaccine meets the criteria for a good TB vaccine the epitope selection process is laborious and time-consuming 39 . In another study, the verified Rv0101, Rv3343, and Rv0058 TB antigens were used to create a new multiepitope subunit vaccine.…”

Section: Related Workmentioning

confidence: 99%

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Mubarak,

Ameen,

Hassan

et al. 2024

Sci Rep

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Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.

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“…IPP- or HMBPP-induced γδ T cells can readily produce the anti-tuberculosis cytokines IFN-γ and TNF-α, which are capable of inducing macrophage or monocyte activation and play important roles in the body's antitumour, anti-infection, immunomodulation, immunosurveillance, and maintenance of immune tolerance abilities [ 8 – 11 ]. Although CD4 + and CD8 + αβ T cells have been clearly demonstrated to play an important role in protective immunity against Mtb infection [ 12 , 13 ], several lines of evidence over the past few years [ 14 – 16 ], including previous work from our laboratory [ 17 , 18 ], suggest that γδ T cells may also play an important role in host immunity against Mtb infection.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the components of Mycobacterium tuberculosis heat-resistant antigen (Mtb-HAg) and its regulation of γδ T-cell function

Wei,

Guo,

Song

et al. 2024

Cell Mol Biol Lett

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Background Mycobacterium tuberculosis heat-resistant antigen (Mtb-HAg) is a peptide antigen released from the mycobacterial cytoplasm into the supernatant of Mycobacterium tuberculosis (Mtb) attenuated H37Ra strain after autoclaving at 121 °C for 20 min. Mtb-HAg can specifically induce γδ T-cell proliferation in vitro. However, the exact composition of Mtb-HAg and the protein antigens that are responsible for its function are currently unknown. Methods Mtb-HAg extracted from the Mtb H37Ra strain was subjected to LC‒MS mass spectrometry. Twelve of the identified protein fractions were recombinantly expressed in Escherichia coli by genetic engineering technology using pET-28a as a plasmid and purified by Ni–NTA agarose resin to stimulate peripheral blood mononuclear cells (PBMCs) from different healthy individuals. The proliferation of γδ T cells and major γδ T-cell subset types as well as the production of TNF-α and IFN-γ were determined by flow cytometry. Their proliferating γδ T cells were isolated and purified using MACS separation columns, and Mtb H37Ra-infected THP-1 was co-cultured with isolated and purified γδ T cells to quantify Mycobacterium viability by counting CFUs. Results In this study, Mtb-HAg from the attenuated Mtb H37Ra strain was analysed by LC‒MS mass spectrometry, and a total of 564 proteins were identified. Analysis of the identified protein fractions revealed that the major protein components included heat shock proteins and Mtb-specific antigenic proteins. Recombinant expression of 10 of these proteins in by Escherichia coli genetic engineering technology was used to successfully stimulate PBMCs from different healthy individuals, but 2 of the proteins, EsxJ and EsxA, were not expressed. Flow cytometry results showed that, compared with the IL-2 control, HspX, GroEL1, and GroES specifically induced γδ T-cell expansion, with Vγ2δ2 T cells as the main subset, and the secretion of the antimicrobial cytokines TNF-α and IFN-γ. In contrast, HtpG, DnaK, GroEL2, HbhA, Mpt63, EsxB, and EsxN were unable to promote γδ T-cell proliferation and the secretion of TNF-α and IFN-γ. None of the above recombinant proteins were able to induce the secretion of TNF-α and IFN-γ by αβ T cells. In addition, TNF-α, IFN-γ-producing γδ T cells inhibit the growth of intracellular Mtb. Conclusion Activated γδ T cells induced by Mtb-HAg components HspX, GroES, GroEL1 to produce TNF-α, IFN-γ modulate macrophages to inhibit intracellular Mtb growth. These data lay the foundation for subsequent studies on the mechanism by which Mtb-HAg induces γδ T-cell proliferation in vitro, as well as the development of preventive and therapeutic vaccines and rapid diagnostic reagents.

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In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis

Cited by 20 publications

References 141 publications

Design and development of a multi‐epitope vaccine for the prevention of latent tuberculosis infection

Design and development of a multi‐epitope vaccine for the prevention of latent tuberculosis infection

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Analysis of the components of Mycobacterium tuberculosis heat-resistant antigen (Mtb-HAg) and its regulation of γδ T-cell function

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