Design of multi-epitope based vaccine against Mycobacterium tuberculosis: a subtractive proteomics and reverse vaccinology based immunoinformatics approach

Phosphorylation at the D56 residue of MtrA in Mycobacterium tuberculosis enhances its DNA binding affinity by modulating inter-domain interaction

The response regulator, MtrA, plays a major role in adaptation to the host environment, cell division, replication, and dormancy activation of Mycobacterium tuberculosis (Mtb). The phosphorylation of the response regulator MtrA alters the downstream activity, typically involving changes in DNA binding activity. However, there is a substantial knowledge gap in understanding the phosphorylation-mediated structural changes in MtrA. Additionally, the active conformation of the protein has yet to be determined. Therefore, in this study, we have investigated the phosphorylation-induced conformational changes of MtrA using all-atom molecular dynamics simulations under various phosphorylation conditions. The results from this study demonstrate that the phosphorylation at D56 (pD56-MtrA) increases the compactness of the MtrA protein by stabilizing the inter-domain interaction between the regulatory domain and DNA binding domain. Notably, the higher occupancy H-bond (over 95 %) between Arg200-Asn100 in case of the pD56-MtrA condition, which is otherwise absent in the non-phosphorylated (uMtrA) condition, suggests the importance of this interaction in the active conformation of the protein. The dynamic cross-correlation analysis reveals that phosphorylation (especially pD56-MtrA) reduces the anti-correlated motions and increases correlated motions between different domains. Moreover, the higher DNA binding affinity of pD56-MtrA compared to uMtrA supported by molecular docking and MD simulation followed by MMPBSA analysis suggests that pD56-MtrA is the possible active conformation of the MtrA protein. Overall, this investigation elucidates the key structural changes in MtrA under different phosphorylated conditions, which might help in designing novel therapeutics against tuberculosis.

Discovering peptides and computational investigations of a multiepitope vaccine target Mycobacterium tuberculosis

Mycobacterium tuberculosis (MTB) is the causative agent of tuberculosis (TB), a prevalent airborne infectious disease. Despite the availability of the Bacille Calmette-Guerin vaccine, its global efficacy remains modest, and tuberculosis persists as a significant global public health threat. Addressing this challenge and advancing towards the End MTB Strategy, we developed a multiepitope vaccine (MEV) based on immunoinformatics and computational approaches. Immunoinformatics screening of MBT protein identified immune-dominant epitopes based on Major Histocompatibility Complex (MHC) allele binding, immunogenicity, antigenicity, allergenicity, toxicity, and cytokine inducibility. Selected epitopes were integrated into an MEV construct with adjuvant and linkers, forming a fully immunogenic vaccine candidate. Comprehensive analyses encompassed the evaluation of immunological and physicochemical properties, determination of tertiary structure, molecular docking with Toll-Like Receptors (TLR), molecular dynamics (MD) simulations for all atoms, and immune simulations. Our MEV comprises 534 amino acids, featuring 6 cytotoxic T lymphocyte, 8 helper T lymphocyte, and 7 linear B lymphocyte epitopes, demonstrating high antigenicity and stability. Notably, molecular docking studies and triplicate MD simulations revealed enhanced interactions and stability of MEV with the TLR4 complex compared to TLR2. In addition, the immune simulation indicated the capacity to effectively induce elevated levels of antibodies and cytokines, emphasizing the vaccine's robust immunogenic response. This study presents a promising MEV against TB, exhibiting favorable immunological and physicochemical attributes. The findings provide theoretical support for TB vaccine development. Our study aligns with the global initiative of the End MTB Strategy, emphasizing its potential impact on addressing persistent challenges in TB control.

Immunoinformatics and structural aided approach to develop multi-epitope based subunit vaccine against Mycobacterium tuberculosis

Tuberculosis is a highly contagious disease caused by Mycobacterium tuberculosis (Mtb), which is one of the prominent reasons for the death of millions worldwide. The bacterium has a substantially higher mortality rate than other bacterial diseases, and the rapid rise of drug-resistant strains only makes the situation more concerning. Currently, the only licensed vaccine BCG (Bacillus Calmette-Guérin) is ineffective in preventing adult pulmonary tuberculosis prophylaxis and latent tuberculosis re-activation. Therefore, there is a pressing need to find novel and safe vaccines that provide robust immune defense and have various applications. Vaccines that combine epitopes from multiple candidate proteins have been shown to boost immunity against Mtb infection. This study applies an immunoinformatic strategy to generate an adequate multi-epitope immunization against Mtb employing five antigenic proteins. Potential B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were speculated from the intended proteins and coupled with 50 s ribosomal L7/L12 adjuvant, and the vaccine was constructed. The vaccine's physicochemical profile demonstrates antigenic, soluble, and non-allergic. In the meantime, docking, molecular dynamics simulations, and essential dynamics analysis revealed that the multi-epitope vaccine structure interacted strongly with Toll-like receptors (TLR2 and TLR3). MM-PBSA analysis was performed to ascertain the system's intermolecular binding free energies accurately. The immune simulation was applied to the vaccine to forecast its immunogenic profile. Finally, in silico cloning was used to validate the vaccine's efficacy. The immunoinformatics analysis suggests the multi-epitope vaccine could induce specific immune responses, making it a potential candidate against Mtb. However, validation through the in-vivo study of the developed vaccine is essential to assess its efficacy and immunogenicity profile, which will assure active protection against Mtb.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection

Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

A comprehensive approach to developing a multi-epitope vaccine against Mycobacterium tuberculosis: from in silico design to in vitro immunization evaluation

Introduction

The Bacillus Calmette-Guérin (BCG) vaccine, currently used against tuberculosis (TB), exhibits inconsistent efficacy, highlighting the need for more potent TB vaccines.

Materials and methods

In this study, we employed reverse vaccinology techniques to develop a promising multi-epitope vaccine (MEV) candidate, called PP13138R, for TB prevention. PP13138R comprises 34 epitopes, including B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes. Using bioinformatics and immunoinformatics tools, we assessed the physicochemical properties, structural features, and immunological characteristics of PP13138R.

Results

The vaccine candidate demonstrated excellent antigenicity, immunogenicity, and solubility without any signs of toxicity or sensitization. In silico analyses revealed that PP13138R interacts strongly with Toll-like receptor 2 and 4, stimulating innate and adaptive immune cells to produce abundant antigen-specific antibodies and cytokines. In vitro experiments further supported the efficacy of PP13138R by significantly increasing the population of IFN-γ+ T lymphocytes and the production of IFN-γ, TNF-α, IL-6, and IL-10 cytokines in active tuberculosis patients, latent tuberculosis infection individuals, and healthy controls, revealing the immunological characteristics and compare the immune responses elicited by the PP13138R vaccine across different stages of Mycobacterium tuberculosis infection.

Conclusion

These findings highlight the potential of PP13138R as a promising MEV candidate, characterized by favorable antigenicity, immunogenicity, and solubility, without any toxicity or sensitization.

Re-Evaluating Human Cytomegalovirus Vaccine Design: Prediction of T Cell Epitopes

HCMV vaccine development has traditionally focused on viral antigens identified as key targets of neutralizing antibody (NAb) and/or T cell responses in healthy adults with chronic HCMV infection, such as glycoprotein B (gB), the glycoprotein H-anchored pentamer complex (PC), and the unique long 83 (UL83)-encoded phosphoprotein 65 (pp65). However, the protracted absence of a licensed HCMV vaccine that reduces the risk of infection in pregnancy regardless of serostatus warrants a systematic reassessment of assumptions informing vaccine design. To illustrate this imperative, we considered the hypothesis that HCMV proteins infrequently detected as targets of T cell responses may contain important vaccine antigens. Using an extant dataset from a T cell profiling study, we tested whether HCMV proteins recognized by only a small minority of participants encompass any T cell epitopes. Our analyses demonstrate a prominent skewing of T cell responses away from most viral proteins-although they contain robust predicted CD8 T cell epitopes-in favor of a more restricted set of proteins. Our findings raise the possibility that HCMV may benefit from evading the T cell recognition of certain key proteins and that, contrary to current vaccine design approaches, including them as vaccine antigens could effectively take advantage of this vulnerability.