In silico design of Mycobacterium tuberculosis epitope ensemble vaccines

Modeling of MT. P495, an mRNA-based vaccine against the phosphate-binding protein PstS1 of Mycobacterium tuberculosis

Tuberculosis (TB) is a contagious disease that predominantly affects the lungs, but can also spread to other organs via the bloodstream. TB affects about one-fourth population of the world. With age, the effectiveness of Bacillus Calmette-Guérin (BCG), the only authorized TB vaccine, decreases. In the quest for a prophylactic and immunotherapeutic vaccine, in this study, a hypothetical mRNA vaccine is delineated, named MT. P495, implementing in silico and immunoinformatics approaches to evaluate key aspects and immunogenic epitopes across the PstS1, a highly conserved periplasmic protein of Mycobacterium tuberculosis (Mtb). PstS1 elicited the potential to generate 99.9% population coverage worldwide. The presence of T- and B-cell epitopes across the PstS1 protein were validated using several computational prediction tools. Molecular docking and dynamics simulation confirmed stable epitope-allele interaction. Immune cell response to the antigen clearance rate was verified by the in silico analysis of immune simulation. Codon optimization confirmed the efficient translation of the mRNA in the host cell. With Toll-like receptors, the vaccine exhibited stable and strong interactions. Findings suggest that the MT. P495 vaccine probably will elicit specific immune responses against Mtb. This mRNA vaccine model is a ready source for further wet-lab validation to confirm the efficacy of this proposed vaccine candidate.

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

Tuberculosis (TB) is a global health threat, killing approximately 1.5 million people each year. The eradication of Mycobacterium tuberculosis, the main causative agent of TB, is increasingly challenging due to the emergence of extensive drug-resistant strains. Vaccination is considered an effective way to protect the host from pathogens, but the only clinically approved TB vaccine, Bacillus Calmette-Guérin (BCG), has limited protection in adults. Multi-epitope vaccines have been found to enhance immunity to diseases by selectively combining epitopes from several candidate proteins. This study aimed to design a multi-epitope vaccine against TB using an immuno-informatics approach. Through functional enrichment, we identified eight proteins secreted by M. tuberculosis that are either required for pathogenesis, secreted into extracellular space, or both. We then analyzed the epitopes of these proteins and selected 16 helper T lymphocyte epitopes with interferon-γ inducing activity, 15 cytotoxic T lymphocyte epitopes, and 10 linear B-cell epitopes, and conjugated them with adjuvant and Pan HLA DR-binding epitope (PADRE) using appropriate linkers. Moreover, we predicted the tertiary structure of this vaccine, its potential interaction with Toll-Like Receptor-4 (TLR4), and the immune response it might elicit. The results showed that this vaccine had a strong affinity for TLR4, which could significantly stimulate CD4⁺ and CD8⁺ cells to secrete immune factors and B lymphocytes to secrete immunoglobulins, so as to obtain good humoral and cellular immunity. Overall, this multi-epitope protein was predicted to be stable, safe, highly antigenic, and highly immunogenic, which has the potential to serve as a global vaccine against TB.

Advance in strategies to build efficient vaccines against tuberculosis

Tuberculosis is a chronic consumptive infectious disease, which can cause great damage to human and animal health all over the world. The emergence of multi-drug resistant strains, the unstable protective effect of Bacillus Calmette-Guérin (BCG) vaccine on adults, and the mixed infection with HIV all warn people to exploit new approaches for conquering tuberculosis. At present, there has been significant progress in developing tuberculosis vaccines, such as improved BCG vaccine, subunit vaccine, DNA vaccine, live attenuated vaccine and inactivated vaccine. Among these candidate vaccines, there are some promising vaccines to improve or replace BCG vaccine effect. Meanwhile, the application of adjuvants, prime-boost strategy, immunoinformatic tools and targeting components have been studied concentratedly, and verified as valid means of raising the efficiency of tuberculosis vaccines as well. In this paper, the latest advance in tuberculosis vaccines in recent years is reviewed to provide reliable information for future tuberculosis prevention and treatment.

To Affinity and Beyond: A Personal Reflection on the Design and Discovery of Drugs

Faced with new and as yet unmet medical need, the stark underperformance of the pharmaceutical discovery process is well described if not perfectly understood. Driven primarily by profit rather than societal need, the search for new pharmaceutical products—small molecule drugs, biologicals, and vaccines—is neither properly funded nor sufficiently systematic. Many innovative approaches remain significantly underused and severely underappreciated, while dominant methodologies are replete with problems and limitations. Design is a component of drug discovery that is much discussed but seldom realised. In and of itself, technical innovation alone is unlikely to fulfil all the possibilities of drug discovery if the necessary underlying infrastructure remains unaltered. A fundamental revision in attitudes, with greater reliance on design powered by computational approaches, as well as a move away from the commercial imperative, is thus essential to capitalise fully on the potential of pharmaceutical intervention in healthcare.

Evaluating the Performance of PPE44, HSPX, ESAT-6 and CFP-10 Factors in Tuberculosis Subunit Vaccines

Mycobacterium tuberculosis (M. tuberculosis) is an intracellular pathogen causing long-term infection in humans that mainly attacks macrophages and can escape from the immune system with the various mechanisms. The only FDA-approved vaccine against M. tuberculosis (MTB) is Mycobacterium bovis bacillus Calmette-Guérin (BCG). The protection of this vaccine typically lasts 10–15 years. Due to the increasing number of people becoming ill with MTB each year worldwide, the need to develop a new effective treatment against the disease has been increased. During the past two decades, the research budget for TB vaccine has quadrupled to over half a billion dollars. Most of these research projects were based on amplifying and stimulating the response of T-cells and developing the subunit vaccines. Additionally, these studies have demonstrated that secretory and immunogenic proteins of MTB play a key role in the pathogenesis of the bacteria. Therefore, these proteins were used to develop the new subunit vaccines. In this review, based on the use of these proteins in the successful new subunit vaccines, the PPE44, HSPX, CFP-10 and ESAT-6 antigens were selected and the role of these antigens in designing and developing new subunit vaccines against TB and for the prevention of TB were investigated.

An Immunoinformatic Strategy to Develop New Mycobacterium tuberculosis Multi-epitope Vaccine

Mycobacterium tuberculosis causes a life-threatening disease known as tuberculosis (TB). In 2021, tuberculosis was the second cause of death after COVID-19 among infectious diseases. Latent life cycle and development of multidrug resistance in one hand and lack of an effective vaccine in another hand have made TB a global health issue. Here, a multi-epitope vaccine have been designed against TB using five new antigenic protein and immunoinformatic tools. To do so, immunodominant MHC-I/MHC-II binding epitopes of Rv2346, Rv2347, Rv3614, Rv3615 and Rv2031 antigenic proteins have been selected using advanced computational procedures. The vaccine was designed by linking ten epitopes from the antigenic proteins and flagellin and TpD as adjuvant. Three-dimensional (3D) structure of the vaccine was modeled, was refined and was evaluated using bioinformatics tools. The 3D structure of the vaccine was docked into the toll-like-receptors (TLR3, 4, 8) to evaluate potential interaction between the vaccine and TLRs. Evaluation of immunological and physicochemical properties of the constructed vaccine have demonstrated the vaccine construct can induce significant humoral and cellular immune responses, the vaccine is non-allergenic and can be recognized by TLR proteins. The immunoinformatic results reported in the present study demonstrates that it is worth following the designed vaccine by experimental investigations.

In silico designed Staphylococcus aureus B-cell multi-epitope vaccine did not elicit antibodies against target antigens suggesting multi-domain approach

The vaccine development strategies have evolved from using an entire organism as an immunogen to a single antigen and further towards an epitope. Since an epitope is a relatively tiny and immunologically relevant part of an antigen, it has the potential to stimulate more robust and specific immune responses while causing minimal adverse effects. As a result, the recent focus of vaccine development has been to develop multi-epitope vaccines that can target multiple virulence mechanisms. Accordingly, we designed multi-epitope vaccine candidates B (multi-B-cell epitope immunogen) and CTB-B (an adjuvant - cholera toxin subunit B (CTB) - attached to immunogen B) against S. aureus by employing immunoinformatics approaches. The designed vaccines are composed of B-cell epitope segments (20-mer) of the eight well-characterized S. aureus virulence factors, namely ClfB, FnbpA, Hla, IsdA, IsdB, LukE, SdrD, and SdrE connected in series. The designed vaccines were expressed, purified, and administered to C57BL/6 mice with Freund adjuvant to evaluate the immunogenicity and protective efficacy. The results revealed that the immunized mice showed high IgG titer for the immunogen, and the antibody titer increased significantly following the second immunization. However, the generated antibodies did not protect mice from infection. The interaction of anti-B antibodies with source virulence factors showed that the generated antibodies have no binding affinity with any of the corresponding virulence factors. Our results demonstrate the limitation of in silico designed B-cell multi-epitope vaccine and suggest that a protein domain carrying both linear and conformational B-cell epitopes might be a better choice for developing an effective multi-epitope vaccine against S. aureus.

Identification and evaluation of immunogenic MHC-I and MHC-II binding peptides from Mycobacterium tuberculosis

Due to several limitations of the only available BCG vaccine, to generate adequate protective immune responses, it is important to develop potent and cost-effective vaccines against tuberculosis (TB). In this study, we have used an immune-informatics approach to identify potential peptide based vaccine targets against TB. The proteome of Mycobacterium tuberculosis (Mtb), the causative agent of TB, was analyzed for secretory or surface localized antigenic proteins as potential vaccine candidates. The T- and B-cell epitopes as well as MHC molecule binding efficiency were identified and mapped in the modelled structures of the selected proteins. Based on antigenicity score and molecular dynamic simulation (MD) studies two peptides namely Pep-9 and Pep-15 were analyzed, modelled and docked with MHC-I and MHC-II structures. Both peptides exhibited no cytotoxicity and were able to induce proinflammatory cytokine secretion in stimulated macrophages. The molecular docking, MD and in-vitro studies of the predicted B and T-cell epitopes of Pep-9 and Pep-15 peptides with the modelled MHC structures exhibited strong binding affinity and antigenic properties, suggesting that the complex is stable, and that these peptides can be considered as a potential candidates for the development of vaccine against TB.

Recent Advances in the Development of Protein- and Peptide-Based Subunit Vaccines against Tuberculosis

The World Health Organization (WHO) herald of the “End TB Strategy” has defined goals and targets for tuberculosis prevention, care, and control to end the global tuberculosis endemic. The emergence of drug resistance and the relative dreadful consequences in treatment outcome has led to increased awareness on immunization against Mycobacterium tuberculosis (Mtb). However, the proven limited efficacy of Bacillus Calmette-Guérin (BCG), the only licensed vaccine against Mtb, has highlighted the need for alternative vaccines. In this review, we seek to give an overview of Mtb infection and failure of BCG to control it. Afterward, we focus on the protein- and peptide-based subunit vaccine subtype, examining the advantages and drawbacks of using this design approach. Finally, we explore the features of subunit vaccine candidates currently in pre-clinical and clinical evaluation, including the antigen repertoire, the exploited adjuvanted delivery systems, as well as the spawned immune response.

Computational approaches in epitope design using DNA binding proteins as vaccine candidate in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) is a successful pathogen in the history of mankind. A high rate of mortality and morbidity raises the need for vaccine development. Mechanism of pathogenesis, survival strategy and virulence determinant are needed to be explored well for this pathogen. The involvement of DNA binding proteins in the regulation of virulence genes, transcription, DNA replication, repair make them more significant. In present work, we have identified 1453 DNA binding proteins (DBPs) in the 4173 genes of Mtb through the DNABIND tool and they were subjected for further screening by incorporating different bioinformatics tools. The eighteen DBPs were selected for the B-cell epitope prediction by using ABCpred server. Moreover, the B-cell epitope bearing the antigenic and non- allergenic property were selected for T-cell epitope prediction using ProPredI, and ProPred server. Finally, DGIGSAVSV (Rv1088), IRALPSSRH (Rv3923c), LTISPIANS (Rv3235), VQPSGKGGL (Rv2871) VPRPGPRPG (Rv2731) and VGQKINPHG (Rv0707) were identified as T-cell epitopes. The structural modelling of these epitopes and DBPs was performed to ensure the localization of these epitopes on the respective proteins. The interaction studies of these epitopes with human HLA confirmed their validation to be used as potential vaccine candidates. Collectively, these results revealed that the DBPs- Rv2731, Rv3235, Rv1088, Rv0707, Rv3923c and Rv2871 are the most appropriate vaccine candidates. In our knowledge, it is the first report of using the DBPs of Mtb for epitope prediction. Significantly, this study also provides evidence to be useful for designing a peptide-based vaccine against tuberculosis.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

Identification of immunogenic T-cell peptides of Mycobacterium tuberculosis PE_PGRS33 protein

The development of a more efficient vaccine is needed to improve tuberculosis control. One of the current approaches is to identify immunogenic T-cell peptides that can elicit a protective and specific immune response. These peptides come from immunogenic proteins of the pathogen. The PE_PGRS33 protein of Mycobacterium tuberculosis has been proved immunogenic. However, little is known about immunogenic T-cell peptides of PE_PGRS33 and their interactions with MHC-II molecules. Therefore, we used the SYFPHEITHI database to determine the immunogenic PE_PGRS33 T-cell peptides. Next, we built homology models by using MOE v2018.1 software in order to obtain information about the specific interactions between the peptides and I-A^k. The AlgPred server was employed to look for allergenic sites in PE_PGRS33. We developed a sequence alignment between PE_PGRS33 and all the human proteins by using BLAST. Three peptides were commercially synthesized, and their activity was evaluated in vitro by the stimulation of PBMC from household contacts of TB patients. Our in silico results showed five immunogenic T-cell peptides. BLAST analysis showed low homology of PE_PGRS33 with human proteins and AlgPred did not reveal allergenic sites in PE_PGRS33. The three peptides triggered the activation of CD4⁺ T cells from the households contacts, showed by the production of IFN-γ. We identified three immunogenic peptides of PE_PGRS33 that demonstrated activity in vitro which allows to deepen into the immune response towards mycobacterial antigens, moving forward to the identification of new vaccine candidates.

Prediction of Epitope-Based Peptide Vaccine Against the Chikungunya Virus by Immuno-informatics Approach

BACKGROUND

Chikungunya is an arthropod-borne viral disease characterized by abrupt onset of fever frequently accompanied by joint pain, which has been identified in over 60 countries in Africa, the Americas, Asia, and Europe.

METHODS

Regardless of the availability of molecular knowledge of this virus, no definite vaccine or other remedial agents have been developed yet. In the present study, a combination of B-cell and T-cell epitope predictions, followed by molecular docking simulation approach has been carried out to design a potential epitope-based peptide vaccine, which can trigger a critical immune response against the viral infections.

RESULTS

A total of 52 sequences of E1 glycoprotein from the previously reported isolates of Chikungunya outbreaks were retrieved and examined through in silico methods to identify a potential B-cell and T-cell epitope. From the two separate epitope prediction servers, five potential B-cell epitopes were selected, among them "NTQLSEAHVEKS" was found highly conserved across strains and manifests high antigenicity with surface accessibility, flexibility, and hydrophilicity. Similarly, two highly conserved, non-allergenic, non-cytotoxic putative T-cell epitopes having maximum population coverage were screened to bind with the HLA-C 12*03 molecule. Molecular docking simulation revealed potential T-cell based epitope "KTEFASAYR" as a vaccine candidate for this virus.

CONCLUSION

A combination of these B-cell and T-cell epitope-based vaccine can open up a new skyline with broader therapeutic application against Chikungunya virus with further experimental and clinical investigation.

West Nile Virus Vaccine Design by T Cell Epitope Selection: In Silico Analysis of Conservation, Functional Cross-Reactivity with the Human Genome, and Population Coverage

West Nile Virus (WNV) causes a debilitating and life-threatening neurological disease in humans. Since its emergence in Africa 50 years ago, new strains of WNV and an expanding geographical distribution have increased public health concerns. There are no licensed therapeutics against WNV, limiting effective infection control. Vaccines represent the most efficacious and efficient medical intervention known. Epitope-based vaccines against WNV remain significantly underexploited. Here, we use a selection protocol to identify a set of conserved prevalidated immunogenic T cell epitopes comprising a putative WNV vaccine. Experimentally validated immunogenic WNV epitopes and WNV sequences were retrieved from the IEDB and West Nile Virus Variation Database. Clustering and multiple sequence alignment identified a smaller subset of representative sequences. Protein variability analysis identified evolutionarily conserved sequences, which were used to select a diverse set of immunogenic candidate T cell epitopes. Cross-reactivity and human leukocyte antigen-binding affinities were assessed to eliminate unsuitable epitope candidates. Population protection coverage (PPC) quantified individual epitopes and epitope combinations against the world population. 3 CD8+ T cell epitopes (ITYTDVLRY, TLARGFPFV, and SYHDRRWCF) and 1 CD4+ epitope (VTVNPFVSVATANAKVLI) were selected as a putative WNV vaccine, with an estimated PPC of 97.14%.

Drug Discovery: Today and Tomorrow

Drug discovery continues to underperform relative to unmet medical need. Driven by profit not societal need, the search for new drugs is neither properly funded nor sufficiently systematic. Many innovative approaches are significantly underused yet extant methodology is replete with problems. In and of itself, technical innovation is unlikely to fulfill the potential of drug discovery if the supporting infrastructure remains unchanged.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Computational assembly of a human Cytomegalovirus vaccine upon experimental epitope legacy

Background

Human Cytomegalovirus (HCMV) is a ubiquitous herpesvirus affecting approximately 90% of the world population. HCMV causes disease in immunologically naive and immunosuppressed patients. The prevention, diagnosis and therapy of HCMV infection are thus crucial to public health. The availability of effective prophylactic and therapeutic treatments remain a significant challenge and no vaccine is currently available. Here, we sought to define an epitope-based vaccine against HCMV, eliciting B and T cell responses, from experimentally defined HCMV-specific epitopes.

Results

We selected 398 and 790 experimentally validated HCMV-specific B and T cell epitopes, respectively, from available epitope resources and apply a knowledge-based approach in combination with immunoinformatic predictions to ensemble a universal vaccine against HCMV. The T cell component consists of 6 CD8 and 6 CD4 T cell epitopes that are conserved among HCMV strains. All CD8 T cell epitopes were reported to induce cytotoxic activity, are derived from early expressed genes and are predicted to provide population protection coverage over 97%. The CD4 T cell epitopes are derived from HCMV structural proteins and provide a population protection coverage over 92%. The B cell component consists of just 3 B cell epitopes from the ectodomain of glycoproteins L and H that are highly flexible and exposed to the solvent.

Conclusions

We have defined a multiantigenic epitope vaccine ensemble against the HCMV that should elicit T and B cell responses in the entire population. Importantly, although we arrived to this epitope ensemble with the help of computational predictions, the actual epitopes are not predicted but are known to be immunogenic.

In silico Design of an Epitope-Based Vaccine Ensemble for Chagas Disease

Trypanosoma cruzi infection causes Chagas disease, which affects 7 million people worldwide. Two drugs are available to treat it: benznidazole and nifurtimox. Although both are efficacious against the acute stage of the disease, this is usually asymptomatic and goes undiagnosed and untreated. Diagnosis is achieved at the chronic stage, when life-threatening heart and/or gut tissue disruptions occur in ~30% of those chronically infected. By then, the drugs' efficacy is reduced, but not their associated high toxicity. Given current deficiencies in diagnosis and treatment, a vaccine to prevent infection and/or the development of symptoms would be a breakthrough in the management of the disease. Current vaccine candidates are mostly based on the delivery of single antigens or a few different antigens. Nevertheless, due to the high biological complexity of the parasite, targeting as many antigens as possible would be desirable. In this regard, an epitope-based vaccine design could be a well-suited approach. With this aim, we have gone through publicly available databases to identify T. cruzi epitopes from several antigens. By means of a computer-aided strategy, we have prioritized a set of epitopes based on sequence conservation criteria, projected population coverage of Latin American population, and biological features of their antigens of origin. Fruit of this analysis, we provide a selection of CD8⁺ T cell, CD4⁺ T cell, and B cell epitopes that have <70% identity to human or human microbiome protein sequences and represent the basis toward the development of an epitope-based vaccine against T. cruzi.

Immunogenicity and Vaccine Potential of InsB, an ESAT-6-Like Antigen Identified in the Highly Virulent Mycobacterium tuberculosis Beijing K Strain

Our group recently identified InsB, an ESAT-6-like antigen belonging to the Mtb9.9 subfamily within the Esx family, in the Mycobacterium tuberculosis Korean Beijing strain (Mtb K) via a comparative genomic analysis with that of the reference Mtb H37Rv and characterized its immunogenicity and its induced immune response in patients with tuberculosis (TB). However, the vaccine potential of InsB has not been fully elucidated. In the present study, InsB was evaluated as a subunit vaccine in comparison with the most well-known ESAT-6 against the hypervirulent Mtb K. Mice immunized with InsB/MPL-DDA exhibited an antigen-specific IFN-γ response along with antigen-specific effector/memory T cell expansion in the lungs and spleen upon antigen restimulation. In addition, InsB immunization markedly induced multifunctional Th1-type CD4⁺ T cells coexpressing TNF-α, IL-2, and IFN-γ in the lungs following Mtb K challenge. Finally, we found that InsB immunization conferred long-term protection against Mtb K comparable to that conferred by ESAT-6 immunization, as evidenced by a similar level of CFU reduction in the lung and spleen and reduced lung inflammation. These results suggest that InsB may be an excellent vaccine antigen component for developing a multiantigenic Mtb subunit vaccine by generating Th1-biased memory T cells with a multifunctional capacity and may confer durable protection against the highly virulent Mtb K.

Ensemble Technique for Prediction of T-cell Mycobacterium tuberculosis Epitopes

Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.