Insight Into Novel Anti-tuberculosis Vaccines by Using Immunoinformatics Approaches

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

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.

Antigen identification strategies and preclinical evaluation models for advancing tuberculosis vaccine development

In its myriad devastating forms, Tuberculosis (TB) has existed for centuries, and humanity is still affected by it. Mycobacterium tuberculosis (M. tuberculosis), the causative agent of TB, was the foremost killer among infectious agents until the COVID-19 pandemic. One of the key healthcare strategies available to reduce the risk of TB is immunization with bacilli Calmette-Guerin (BCG). Although BCG has been widely used to protect against TB, reports show that BCG confers highly variable efficacy (0-80%) against adult pulmonary TB. Unwavering efforts have been made over the past 20 years to develop and evaluate new TB vaccine candidates. The failure of conventional preclinical animal models to fully recapitulate human response to TB, as also seen for the failure of MVA85A in clinical trials, signifies the need to develop better preclinical models for TB vaccine evaluation. In the present review article, we outline various approaches used to identify protective mycobacterial antigens and recent advancements in preclinical models for assessing the efficacy of candidate TB vaccines.

Immunoinformatics design of multi-epitope vaccine using OmpA, OmpD and enterotoxin against non-typhoidal salmonellosis

BACKGROUND

Non-typhoidal Salmonella (NTS) is one of the important bacteria that cause foodborne diseases and invasive infections in children and elderly people. Since NTS infection is difficult to control due to the emergence of antibiotic-resistant species and its adverse effect on immune response, the development of a vaccine against NTS would be necessary. This study aimed to develop a multi-epitope vaccine against the most prevalent serovars of NTS (Salmonella Typhimurium, Salmonella Enteritidis) using an immunoinformatics approach and targeting OmpA, OmpD, and enterotoxin (Stn).

RESULTS

Initially, the B cell and T cell epitopes were predicted. Then, epitopes and suitable adjuvant were assembled by molecular linkers to construct a multi-epitope vaccine. The computational tools predicted the tertiary structure, refined the tertiary structure and validated the final vaccine construct. The effectiveness of the vaccine was evaluated via molecular docking, molecular dynamics simulation, and in silico immune simulation. The vaccine model had good binding affinity and stability with MHC-I, MHC-II, and toll-like receptors (TLR-1, 2, 4) as well as activation of T cells, IgM, IgG, IFN-γ and IL-2 responses. Furthermore, after codon optimization of the vaccine sequence, this sequence was cloned in E. coli plasmid vector pET-30a (+) within restriction sites of HindIII and BamHI.

CONCLUSIONS

This study, for the first time, introduced a multi-epitope vaccine based on OmpA, OmpD and enterotoxin (Stn) of NTS that could stimulate T and B cell immune responses and produced in the prokaryotic system. This vaccine was validated in-silico phase which is an essential study to reduce challenges before in vitro and in vivo studies.

Exploring staphylococcal superantigens to design a potential multi-epitope vaccine against Staphylococcus aureus: an in-silico reverse vaccinology approach

Staphylococcus aureus is a horrifying bacteria capable of causing millions of deaths yearly across the globe. A major contribution to the success of S. aureus as an ESKAPE pathogen is the abundance of virulence factors that can manipulate the innate and adaptive immune system of the individual. Currently, no vaccine is available to treat S. aureus-mediated infections. In this study, we present in-silico approaches to design a stable, safe and immunogenic vaccine that could help to control the infections associated with the bacteria. Three vital pathogenic secreted toxins of S. aureus, such as staphylococcal enterotoxin A (SEA), staphylococcal enterotoxin B (SEB), Toxic-shock syndrome toxin (TSST-1), were selected using the reverse vaccinology approach to design the multi-epitope vaccine (MEV). Linear B-lymphocyte, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. For designing the multi-epitope vaccine (MEV), B-cell epitopes were joined with the KK linker, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Finally, to increase the immune response to the vaccine, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminus of the MEV construct. The final MEV was found to be antigenic and non-allergen in nature. In-silico immune simulation and cloning analysis predicted the immune-stimulating potential of the designed MEV construct along with the cloning feasibility in the pET28a(+) vector with the E. coli expression system. This immunoinformatics study provides a platform for designing a suitable, safe and effective vaccine against S. aureus.Communicated by Ramaswamy H. Sarma.

A subtractive proteomics and immunoinformatics approach towards designing a potential multi-epitope vaccine against pathogenic Listeriamonocytogenes

Listeria monocytogenes is the causative agent of listeriosis, which is dangerous for pregnant women, the elderly or individuals with a weakened immune system. Individuals with leukaemia, cancer, HIV/AIDS, kidney transplant and steroid therapy suffer from immunological damage are menaced. World Health Organization (WHO) reports that human listeriosis has a high mortality rate of 20-30% every year. To date, no vaccine is available to treat listeriosis. Thereby, it is high time to design novel vaccines against L. monocytogenes. Here, we present computational approaches to design an antigenic, stable and safe vaccine against the L. monocytogenes that could help to control the infections associated with the pathogen. Three vital pathogenic proteins of L. monocytogenes, such as Listeriolysin O (LLO), Phosphatidylinositol-specific phospholipase C (PI-PLC), and Actin polymerization protein (ActA), were selected using a subtractive proteomics approach to design the multi-epitope vaccine (MEV). A total of 5 Cytotoxic T-lymphocyte (CTL) and 9 Helper T-lymphocyte (HTL) epitopes were predicted from these selected proteins. To design the multi-epitope vaccine (MEV) from the selected proteins, CTL epitopes were joined with the AAY linker, and HTL epitopes were joined with the GPGPG linker. Additionally, a human β-defensin-3 (hBD-3) adjuvant was added to the N-terminal side of the final MEV construct to increase the immune response to the vaccine. The final MEV was predicted to be antigenic, non-allergen and non-toxic in nature. Physicochemical property analysis suggested that the MEV construct is stable and could be easily purified through the E. coli expression system. This in-silico study showed that MEV has a robust binding interaction with Toll-like receptor 2 (TLR2), a key player in the innate immune system. Current subtractive proteomics and immunoinformatics study provides a background for designing a suitable, safe and effective vaccine against pathogenic L. monocytogenes.