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*Corresponding author: E-mail: naseemar8@gmail.com; 
 
Cite as: Naseema. R. 2025. “An Immunocomputing Approach to the Development of Multiepitope Vaccine Against 
Mycobacterium Tuberculosis”. Asian Journal of Immunology 8 (1):269–284. https://doi.org/10.9734/aji/2025/v8i1178. 
 
 

 
 

Asian Journal of Immunology 
 
Volume 8, Issue 1, Page 269-284, 2025; Article no.AJI.147582 
 

 
 

 

 

An Immunocomputing Approach to the 
Development of Multiepitope Vaccine 
against Mycobacterium tuberculosis 

 
Naseema. R a* 

 
a Department of Biotechnology, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India. 

 
Author’s contribution 

 
The sole author designed, analyzed, interpreted and prepared the manuscript. 

 
Article Information 

 
DOI: https://doi.org/10.9734/aji/2025/v8i1178  

 
Open Peer Review History: 

This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers,  
peer review comments, different versions of the manuscript, comments of the editors, etc are available here: 

https://pr.sdiarticle5.com/review-history/147582  

 
 

Received: 17/09/2025 
Published: 24/11/2025 

 
 

ABSTRACT 
 

Tuberculosis (TB) is the second most deadly airborne infectious disease caused by Mycobacterium 
tuberculosis, posing a major global health risk       dw. Nearly one-third of the world’s population is 
infected, with low-income countries most affected due to limited access to diagnostics and 
treatments. Although the Bacillus Calmette–Guérin (BCG) vaccine offers protection in infancy, its 
reduced efficacy beyond 20 years limits long-term TB control. This study focuses on drafting a 
multi-epitope peptide-based vaccine against TB using immune-informatics approaches. Ten M. 
tuberculosis-specific antigenic proteins (PPE39, PPE68, Rv0310c, PE_PGRS35, PE_PGRS31, 
CFP10, Rv1975, lpqG, esxV, and espJ) were analyzed to identify potent immunogenic regions. 
Five cytotoxic T lymphocyte (CTL) epitopes, five helper T cell (HTL) epitopes, and several B-cell 
epitopes are proficient of causing strong immune responses, including Interferon-γ (IFN-γ) 
production, were selected. A total of 27 epitopes were linked using AAY, GPGPG, and KK linkers to 
construct the vaccine, which was assessed for physicochemical properties, antigenicity, 
allergenicity, and toxicity. The design demonstrated stability and strong immunogenic potential. 
Molecular docking with the TLR-4 monomer showed favorable binding affinity, indicating its 

Original Research Article 

https://doi.org/10.9734/aji/2025/v8i1178
https://pr.sdiarticle5.com/review-history/147582


 
 
 
 

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potential as an effective vaccine candidate. By applying immune-informatics tools for precise 
epitope selection and vaccine design, this research provides a cost-effective strategy to enhance 
TB prevention. The study offers a strong framework for developing next-generation TB vaccines 
and supports global efforts to eliminate tuberculosis by 2035. 

 

 
Keywords:  Mycobacterium tuberculosis (M.tb); Cytotoxic T lymphocyte (CTL); Helper T cell (HTL); 

Interferon-γ (IFN-γ); Bacillus Calmette–Guérin (BCG). 
 

1. INTRODUCTION 
 
“Tuberculosis (TB) is an airborne disease caused 
by the bacterium Mycobacterium tuberculosis. M. 
tb and its seven closely related species namely 
M. bovis, M. africanum, M. microti, M. caprae, M. 
pinnipeti, M. canetti and M. munji collectively 
comprises M. tuberculosis complex. But not all of 
these species are reported to cause disease in 
humans” (Beresford & Sadoff, 2010). “The 
majority cases of TB are found to be caused by 
M. tuberculosis (Wheeler et al., 2007). TB 
remains one among the main global health 
threats which causes millions of deaths annually 
across the globe. Majority of affected people hail 
from low income and middle-income countries 
(LMIC) like countries from African sub- continent” 
(Faust et al., 2019). “An estimated 10 million 
people fell ill with TB worldwide in 2019, which 
includes 5.6 million men, 3.2 million women, and 
1.2 million children” (Saha & Raghava, 2006). 
“TB is existing in all Countries and all age 
groups. 5 – 15 % patients infected with only TB 
are likely to develop active TB in their lifetime. TB 
is more severe in HIV patients and is one of the 
main reason for their mortality.  BCG (Bacille 
Calmette - Guerin) is the only vaccine available 
for preventing TB in humans. Although BCG is of 
low-cost and widely used vaccine in many of the 
countries, its potency has varied generally in 
many clinical assays spanned between 0- 80 % 
(Brandt et al., 2002; Pablos-Mendez et al., 2002). 
It offers poor protection in adults and adolescents 
against pulmonary tuberculosis” (Ferraz et al., 
2004). Also failed to treat Latent TB infection 
(LTBI) (Weinreich Olsen et al., 2000). In the 
presence of immune-suppression, reversion of 
virulence may take place and possible induction 
of disease may happen (Sharma et al., 2021). 
For these various reasons, the WHO has framed 
“The End TB Strategy which targets the 
prevention, care and control for Tuberculosis. To 
meet all the requirements and to satisfy all the 
problems associated with Mycobacterium 
tuberculosis, there is an urge to build an efficient 
vaccine, which offers complete safeguard       
against tuberculosis” (World Health Organization, 
2018). 

“In the current study, we have chosen ten 
mycobacterial proteins from different region of 
difference (PPE39, PPE68, Rv0310c, 
PE_PGRS35, PE_PGRS_31, CFP10, Rv1975, 
lpqG, esxV, espJ) and an adjuvant (RpfE) for 
vaccine construct. PE/PPE families are found to 
be associated with pathogenicity of MTB 
infection. As in all pathogenic mycobacteria, the 
members of these families are abundant and 
comparatively shows less presence in non-
pathogenic mycobacteria. It is observed that, 
PE/PPE proteins play a major role in processing 
of macrophages” (Bigi et al., 2000). Adhesion of 
pathogenic proteins on the surface of 
macrophage receptors, immune response to 
pathogenic proteins, resistance of intracellular 
stress, Phagocytosis, intracellular survival in the 
host environment and regulation of cell fate are 
the key events involved in the Host-Pathogen 
interactions.  
 
During various stages of infection, the 
CFP10/ESAT6 complex in Mycobacterium 
tuberculosis could play a role in regulating 
macrophage death (Trott & Olson, 2010). “The 
major advantage in peptide-based vaccines is 
the absence of live or attenuated infectious 
materials. Hence there is zero risk for reversion 
of virulence” (Trunz et al., 2006). “To enhance 
the stability, solubility lipid, carbohydrate and 
phosphates groups can be readily introduced. 
One major advantage of epitope-based vaccines 
is that they rely on a small, precisely defined 
antigen, making it easier to understand and 
evaluate its immunogenicity and antigenicity 
(Choi et al., 2015). The induction of neutralizing 
antibodies and a protective T helper and a CTL 
response by T cell and B cell epitope must be 
required for a good TB vaccine. To maximize 
coverage, the CTL and HTL epitopes were tested 
for their ability to bind with the most prevalent 
human HLA alleles for MHC class I and II 
(Martinot et al., 2016). “The adjuvant RfpE was 
included to the vaccine draft to enhance its 
immunogenicity” (Bellini & Horváti, 2020). 
Several vaccine sequences were generated and 
analyzed for allergenicity, toxicity, antigenicity, 
and important physicochemical properties such 



 
 
 
 

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as isoelectric point, solubility, and half-life in 
different hosts” (Kelley et al., 2015). “Homology 
modeling was carried out for all vaccine 
sequences, and the best 3D model was chosen 
based on the recommendations of online 
servers” (Rapin et al., 2010). The stereochemical 
geometry of this model was then evaluated, both 
at the residue level and overall structure 
(Sanches et al., 2021). Finally, molecular docking 
was performed between the optimized 3D 
vaccine model and TLR4 to study binding 
interactions. 
 

2. METHODS AND METHODOLOGY 
 

1. HTL epitopes prediction: 
 

Helper T lymphocyte (HTL) epitopes were 
forecasted using the NetMHCIIpan 4.0 server, 
which is an online tool for identifying immune 
epitopes based on a large collection of 
experimentally validated data. All protein 
sequences of the chosen antigens were handed 
in simultaneously in FASTA format. The default 
settings were used to predict epitopes across the 
total human HLA reference set, specifying an 
epitope length of 9 amino acids. The predicted 
epitopes were then ranked and scored according 
to their binding affinity. 

 
2.  CTL epitopes prediction: 

 
Cytotoxic T lymphocyte (CTL) epitopes were 
predicted using the NetCTLpan 1.2 server. All 
amino acid sequences from the selected proteins 
were uploaded together in FASTA format. Using 
the full human HLA reference set, 9-mer epitopes 
were predicted with default settings, and the 
resulting peptides were ranked by their scores. 
The chosen epitopes were then examined for 
their allergenic, toxic, and antigenic properties. 

 
MHC I–related HLA-C epitopes were identified 
with the NetCTLpan 1.1 server, with all selected 
protein sequences submitted at once in FASTA 
format.This prediction is based on MHC class – I 
binding affinity, proteasomal cleavage efficiency 
and TAP transport efficiency. 20 alleles of HLA-C 
haplotype were given as input for prediction. 
These 20 alleles were taken as a reference set 
from IEDB. In total five HLA -C epitopes were 
chosen for the vaccine construct.  
 

3. B‑cell epitopes prediction: 
 
The B cell epitopes were predicted using 
ABCpred web server. All the amino acids 

sequences of the selected proteins were 
submitted at a time in fasta format (Hougardy et 
al., 2007). The top 30 epitopes were selected 
based on the score and was further analyzed for 
antigenicity, toxicity and allergenicity. Any 
epitopes flagged as allergenic, toxic, or non-
antigenic by AllerTop v2.0, ToxinPred, or 
VaxiJen were not included in the study. 
Altogether, fourteen B-cell epitopes were chosen 
from the ten selected proteins (Supplementary 
Table 1). 
 

4. IFN-γ analysis: 
 
IFN-γ plays a central role in defending the body 
against intracellular pathogens, and in 
Mycobacterium tuberculosis infection it is 
especially important for activating macrophages 
(Dimitrov et al., 2014). The IFN were predicted 
from the IFN epitope server 
(http://crdd.osdd.net/raghava/ifnepitope/). Here, 
we analyzed IFN-γ activity for all the selected 
epitopes. 
 

5. Vaccine construct preparation: 
 
Five vaccine constructs were randomly designed 
using the adjuvant (RpfE), which was attached to 
the N-terminal end. Each construct followed the 
same layout, placing B-cell, HTL, CTL, and then 
HLA-C epitopes in sequence. The epitopes were 
joined using KK, GPGPG, and AAY linkers, and 
the adjuvant was added using an EAAAK linker. 
 

6. Prediction of epitope’s antigenicity, 
allergenicity and toxicity: 

 
Five vaccine constructs were randomly designed 
using the adjuvant RpfE, which was attached to 
the N-terminal end. In each construct, the 
selected epitopes were arranged in the order of 
B-cell, HTL, CTL, and HLA-C. The B-cell, HTL, 
CTL, and HLA-C epitopes were joined using KK, 
GPGPG, and AAY linkers, respectively, while the 
adjuvant was connected through an EAAAK 
linker, the relative abundance of amino acids, 
and β-strand forming propensity (Doytchinova & 
Flower, 2007). Prediction of antigenicity was 
done by alignment-independent fashion based 
on its physicochemical properties using VaxiJen 
online web server (Gupta et al., 2013). This tool 
provided varied options for the organism selected 
for analysis was bacteria, for which the 
corresponding default threshold score (0.4) was 
applied automatically. The ToxinPred server was 
employed to assess the toxicity of the selected 
sequences, as this tool generates all possible 



 
 
 
 

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mutants of the input peptides for evaluation 
(Branger et al., 2004). All parameters in both 
AllerTOP v.2.0 and ToxinPred were maintained 
at their default settings. 
 

7. Homology modelling of vaccine 
constructs: 

 
The five vaccine constructs were analyzed 
through homology modelling on the SWISS-
MODEL server to identify the most structurally 
stable option. The generated models were saved 
in PDB format for later evaluation. 
 

8. Structure Refinement of Vaccine 
Construct: 

 
The tertiary structure of the selected vaccine 
draft was refined utilizing the CASTp web server. 
CASTp applies structural perturbation and 
molecular dynamics simulations to achieve 
protein relaxation. The most stable and 
accurately refined model was selected for further 
evaluation, including physicochemical property 
assessment and molecular docking studies. 
 

9. Physiochemical properties of vaccine 
construct: 

 
The physicochemical characteristics of the 
vaccine construct were found using the 
ProtParam tool available online. This tool 
evaluates several properties of a protein, 
including   molecular weight, theoretical 
isoelectric point (pI), instability index, protein 
length, estimated half-   life, aliphatic index, and 
the grand average of hydropathicity (GRAVY). 
 

10. Molecular docking of vaccine construct: 
 
The refined 3D structures of the vaccine 
constructs were utilized for molecular docking 
studies. The crystal structure of the human TLR4 
receptor complex (PDB ID: 3FXI) was retrieved 
from the Protein Data Bank. Docking was 
performed using the PyDock web server with the 
monomeric form of the human TLR4 receptor 
(Schorey & Harding, 2016). Additionally, 
AutoDock Vina was employed to perform docking 
using the A-chain of the TLR4 crystal structure 
with the vaccine constructs (Park et al., 2009; 
Heo et al., 2013). Using the CASTp server, we 
identified the active and passive binding pockets 
of the TLR4 receptor and the refined vaccine 
constructs. The corresponding PDB files were 

then uploaded to the PyDock server for docking 
analysis. 
 

 
 
Fig. 1. The schematic illustration of the final 
vaccine construct shows the arrangement of 
epitopes, represented within colored boxes 

and connected by linker sequences. The 
designed construct is made up of 623 amino 
acids, including a 172-amino-acid adjuvant 

(RpfE/Rv2450c) highlighted in yellow. It 
incorporates 14 B-cell epitopes (blue), 5 HTL 

epitopes (orange), 5 CTL epitopes (green), 
and 3 HLA-C epitopes (pink). An EAAAK 

linker attaches the adjuvant to the N-
terminus, while the B-cell, HTL, CTL, and 
HLA-C epitopes are connected using KK, 

GPGPG, and AAY linkers 
 



 
 
 
 

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A)                                                        B) 

 
Fig. 2. Vaccine construct homology modelling and structural refinement: (A) The RpfE vaccine 

construct’s three-dimensional structure was first created using SWISS-MODEL (B) Refined 
version was produced using CASTp (Binkowski et al., 2003) 

 
11. Immune simulation for vaccine efficacy 

 

“C-ImmSim was used to assess the immune 
response and immunogenicity of the multi-
epitope vaccine. The server applies a position-
specific scoring matrix and machine learning, 
along with epitope prediction, to model immune 
interactions. It contains 6,533 antigenic epitopes 
and 33 different human HLA allele sets, 
simulating responses by pairing epitope 
sequences with lymphocyte receptors” (Brosch et 
al., 2007).   “The tool predicts immune responses 
by simulating three important mammalian 
anatomical regions: the thymus, bone marrow, 
and a tertiary lymphoid organ, including the 
spleen, lymph nodes, or tonsils (Mangtani et al., 
2014). The vaccine construct and the PDB files 
for TLR4 and MHC I were uploaded to C-
ImmSim, using the default settings for all 
parameters. 

 

3. RESULTS 
 

1. B-cell epitopes prediction: 
 

A total of thirty B-cell epitopes were first 
predicted with ABCPred and then evaluated for 
their antigenic, toxic, and allergenic properties. 
Epitopes identified as toxic, allergenic, or non-
antigenic through ToxinPred, AllerTOP v.2.0, and 
VaxiJen, respectively, were excluded from further 
analysis. Ultimately, fourteen B-cell epitopes 
were selected for inclusion in the vaccine 
construct. 
 

2. HTL epitopes prediction: 
 

From the initial set of twenty predicted HTL 
epitopes, only those exhibiting antigenic, non-
toxic, and non-allergenic properties were 

considered. Using these criteria, five HTL 
epitopes were selected for inclusion in the 
vaccine construct. 
 

3. CTL epitopes prediction: 
 
Twenty CTL epitopes with the highest binding 
affinity to MHC class I molecules were selected 
for further evaluation of allergenicity, antigenicity, 
and toxicity. Any duplicate epitopes found across 
multiple HLA sets were removed. Only epitopes 
that were non-allergenic, non-toxic, and antigenic 
were retained for downstream analyses. In total, 
five CTL epitopes and three HLA-C epitopes 
derived from PPE39, PPE68, Rv0310c, 
PE_PGRS35, PE_PGRS31, CFP10, Rv1975, 
lpqG, esxV, and espJ were selected for vaccine 
construction. 
 

4. Vaccine construct preparation: 
 
Two random vaccine constructs were generated 
by shuffling the arrangement of the selected 
epitopes. In each construct, the epitopes were 
organized in the order of B-cell, HTL, CTL, and 
HLA-C. The B-cell, HTL, CTL, and HLA-C 
epitopes were connected using KK, GPGPG, and 
AAY linkers, respectively. The adjuvant proteins 
RpfE and RpfB were attached to the N-terminal 
end of the vaccine constructs via an EAAAK 
linker. 
 

5. Homology modelling and selection of 
best vaccine construct: 

 
Homology modelling and structural validation of 
the vaccine constructs were performed using the 
SWISS-MODEL web server. The generated 
models were evaluated based on the 
Ramachandran plot statistics, mean score, and 



 
 
 
 

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Z-score provided by SWISS-MODEL reports. 
Among all constructs, Vaccine Construct 1 
exhibited the most favorable results, with 
residues in the most preferred regions, 
additionally allowed regions, generously allowed 
regions, and disallowed regions accounting for 
93.06%, 5.17%, 1.77%, and 0.0%, respectively. 
While the Z-score, RMSD, MolProbity, clash 
score for Verify3D were -1.10,0.248,1.39 and 6.9 
respectively. The final chosen vaccine construct 
was also analyzed for its antigenicity using 
VaxiJen tool where antigenicity was predicted to 
be 1.0664. The chosen vaccine construct 
sequence is shown in Supplementary Fig.1 and 
the graphical representation of vaccine construct 
in order of epitopes utilized can be found in Fig. 
1. 

6. Vaccine construct tertiary structure 
refinement: 

 
The homology model generated using Swiss 
model (Fig. 2A) is exhibiting the finest 
Ramachandran plot values was exposed to 
improvement using CastP. Out of all the five 
models generated by CastP, model 2 (Fig. 2B) 
was found to be most appropriate where values 
for GDT-HA, RMSD and MOLProbity were 0.993, 
0.248 and 1.378 respectively. After refinement, 
the most preferred regions of Ramachandran 
increased to 93.72% when compared to the 
structure obtained from Swiss model (Fig.3). At 
last, the refined model 2 was chosen for further 
investigations like molecular docking and 
dynamic simulations. 

 

 
 

Fig. 3. Ramachandran plot analysis of the refined tertiary structure of vaccine construct 
obtained from rampage web server. After refinement, most preferred regions increased to 
93.72% as compared to the crude 3D structure derived from Swiss model where the most 

favoured regions were 93.06% 



 
 
 
 

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7. Vaccine constructs physicochemical 
properties:  

 
The selected refined vaccine construct consisted 
of 623 amino acids with an estimated molecular 
weight of 61.83 kDa. Typically, vaccine 
candidates with molecular masses greater than 
50 kDa are preferred, as those with lower 
molecular weights tend to exhibit reduced lymph 
node accumulation. The construct has a 
theoretical pI of 9.10, containing 39 negatively 
and 49 positively charged residues, suggesting a 
basic nature. Its predicted half-life is around 30 
hours in mammalian reticulocytes, over 10 hours 
in E. coli, and over 20 hours in yeast. The 
aliphatic index was determined to be 60.42, and 
the instability index was 31.42, suggesting that 
the protein is thermally stable. The grand 
average of hydropathicity (GRAVY) value was 
calculated as −0.282, indicating the hydrophilic 
nature of the construct. 
 

 
 

Fig. 4. Molecular docking of vaccine 
construct and TLR4 ligand 

 
8. Molecular docking of vaccine construct 

with TLR4: 
 
The CASTp server was employed to identify the 
active and passive ligand-binding sites within the 
refined tertiary structure of the vaccine construct 
(Gülbay et al., 2006). The analysis revealed 62 
active and 91 passive ligand-binding residues in 
TLR4. For the vaccine construct, 129 residues 
were identified as active and 145 as passive 
binding sites. Both the refined construct and 
TLR4 monomers were subsequently submitted to 

the PyDock server for molecular docking. 
PyDock provided top 10 models in which model 1 
was selected. Other calculated parameters are 
provided in Supplementary Table 2. The docked 
vaccine construct and TLR4 receptor can be 
seen in Fig. 4.  
 

9. Molecular docking of vaccine construct 
with MHC I: 

 
To predict the active and passive ligand binding 
sites in the refined tertiary vaccine structure the 
CastP server was used (Martinot et al., 2016). It 
predicted 60 residues as active ligand- binding 
sites and 92 residues as passive ligand- binding 
sites for MHC I. The predicted active ligand-
binding sites and passive ligand-binding sites for 
vaccine construct was 129, 145 residues 
respectively. For molecular docking, the refined 
vaccine construct and MHC I monomers were 
uploaded to PyDock web server. PyDock 
provided top 10 models in which model 1 was 
selected. Other calculated parameters are 
provided in Supplementary Table 3. The docked 
vaccine draft and MHC I receptor can be seen in 
Fig. 5. 
 

 
 

Fig. 5. Molecular docking of vaccine 
construct and MHC I ligand 

 
10. Immune simulation for vaccine efficacy 

 
The immune responses predicted by the C-
ImmSim server indicated a robust immunogenic 
potential for the vaccine construct. The vaccine 
elicited strong primary and secondary immune 
responses, as reflected in the simulation graphs. 
The primary response was characterized by an 



 
 
 
 

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Fig. 6. In silico immune simulation results of vaccine construct with TLR4 ligand using 
C-ImmSim web server (https:// www. iac. cnr. it/ ~filip po/ proje cts/c- immsim- online. 

html). (A) The rapid expansion of B-cells was accompanied by the formation of memory 
B-cells. (B) Elevated level IgG +IgM antibodies in peripheral blood cells.  (C) Elevated 
B-cell (active) production after a lag phase of 5–7 days after exposure to the vaccine 

chimera. (D) High level of T helper cells production in response to antigens (E)Active T 
helper cell population within 5 days of exposure to antigen. (F) Constant production of 
Th1 cells (G) Increased level of cytotoxic T memory cells and (H) Evolution of cytotoxic 

T-cells 



 
 
 
 

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Fig. 7. In silico immune simulation results of vaccine construct with MHC I ligand using C-
ImmSim web server (https:// www. iac. cnr. it/ ~filip po/ proje cts/c- immsim- online. html). (A) 

The rapid proliferation of B-cell along with memory B-cells. (B) High level of IgG +IgM 
antibodies in peripheral blood cells.  (C) Increased B-cell (active) production after a lag phase 

of 5–7 days after exposure to the vaccine chimera. (D) Elevated level of T helper cells 
production in response to antigens (E)Active T helper cell population within 5 days of 

exposure to antigen. (F) Constant production of Th1 cells (G) Higher level of cytotoxic T 
memory cells and (H) Evolution of cytotoxic T-cells 



 
 
 
 

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increase in IgM antibody levels following a lag 
period of 5–7 days post-antigen exposure. The 
secondary response demonstrated enhanced B-
cell proliferation along with elevated levels of 
IgG+IgM, IgM, and IgG1+IgG2 antibodies. In 
addition to promoting significant B-cell 
proliferation and memory B-cell formation, the 
vaccine construct induced strong cytotoxic and 
helper T-cell activity and maintained elevated 
IFN-γ levels for an extended duration. 
 
4. DISCUSSION 

 
Mycobacterium tuberculosis (M. tb) is the second 
deadliest disease which can actively tackle 
antibiotic treatment (Mehla & Ramana, 2016). 
“Globally, there is an urge to develop vaccines 
and treatment for TB, because recently it has 
been identified that the well-known and only 
licensed anti-tuberculosis vaccine called BCG is 
no longer effective in adults” (Mustafa, 2002). “In 
light of ever-growing drug resistance and 
adverse effects associated with anti-TB drugs 
such as ototoxicity, hepatotoxicity, neuro-
psychiatricevents, hyperuricemia, gastrointestinal 
disturbance, vision loss, skin pigmentation etc, a 
safe and efficacious vaccine could be an 
imperious arsenal against this lethal disease” 
(Nagpal et al., 2020). “The different protocols 
followed by various laboratories over the last few 
years for BCG culture showed different level of 
divergence in the efficacy of BCG vaccine” (Lee 
et al., 2014). “Several reports suggest that BCG 
grown in Sauton medium elicits a stronger 
immune response than BCG cultured in 
Middlebrook 7H9 medium” (Fleri et al., 2017). 
“Out of the numerous anti-tuberculosis vaccines 
under development, only VMP1002, MIP, and M. 
vaccae have advanced to phase III trials, with 
plans to use them as boosters for BCG in the 
future. In recent years, the use of 
immunoinformatics has rapidly gained attention 
for drug and vaccine development compared to 
traditional approaches” (Jung et al., 2011). In this 
study, a multi-epitope vaccine was designed 
using experimentally validated immunogenic 
exosome vesicle-based antigens in response to 
the global crisis (Carmona et al., 2013). “To 
assess their immune-stimulating potential, these 
antigens were examined for B-cell, HTL, and 
CTL epitopes, corresponding to humoral, innate, 
and cell-mediated responses. Additionally, all 
selected epitopes were evaluated for non-
toxicity, non-allergenicity, and IFN-γ induction. 
The chosen epitopes were linked using 
appropriate peptide linkers, and the TLR4 
agonist peptide RpfE was attached to the N-

terminal of the construct to serve as an adjuvant 
and enhance the immune response. During M. 
tuberculosis infection, both TLR2 and TLR4 are 
involved in pathogen recognition, activating 
macrophages and dendritic cells and influencing 
both innate and adaptive immunity” (Hart et al., 
1987). “TLR4 was selected as the target receptor 
for the vaccine construct due to its critical role in 
infection; TLR4-deficient mice show lower 
survival rates and higher bacterial loads 
compared to TLR2-deficient mice when infected 
with M. tuberculosis” (Kim et al., 2018). Five 
vaccine constructs were created and evaluated 
using homology modeling. One construct was 
refined in 3D and docked with monomeric TLR4, 
producing a complex with favorable binding 
energy and stable structure. Finally, an immune 
simulation study, modeling the innate immune 
network including T and B cells, was performed 
to evaluate both humoral and cellular responses. 
The results indicate that the designed multi-
epitope vaccine elicits a strong and consistent 
immune response. 
 

5. CONCLUSION 
 
Our results show that the vaccine candidate 
possesses suitable physicochemical qualities, a 
stable structure, and encouraging immunological 
attributes capable of promoting both humoral and 
cellular immunity. Building upon these findings, 
future work will focus on validating the efficacy of 
the proposed vaccine as both a preventive and 
therapeutic candidate through in vivo evaluation 
in a suitable mouse model. Therefore, it should 
be pushed forward as a potential lead candidate 
for in vivo and in vitro assessments against M. tb. 
(Yun et al., 2024; Nayak et al., 2023). 

 
DISCLAIMER (ARTIFICIAL INTELLIGENCE) 

 
Author(s) hereby declare that NO generative AI 
technologies such as Large Language Models 
(ChatGPT, COPILOT, etc) and text-to-image 
generators have been used during writing or 
editing of this manuscript.  

 
COMPETING INTERESTS 
 
Authors have declared that no competing 
interests exist. 

 
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SUPPLEMENTARY TABLE 
 

Vaccine Sequence: 
 
MKNARTTLIAAAIAGTLVTRSPAGIANADDAGLDPNAAAGPDAVGFDPNLPPAPDAAPVDTPPAPEDA
GFDPNLPPPLAPDFLSPPAEEAPPVPVAYSVNWDAIAQCESGGNWSINTGNGYYGGLQFTAGTWRA
NGGSGSAANASREEQIRVAENVLRSQGIRAWPVCGRRGEAAAKAGLWGNGGSGGAGGNAKKAGLI
GNGGAGGAGGPNKKGSPITTLYVKIDGGTPKKGGMGGSGGGIGAGTTTKKNVPIDPSPDYDASDEIK
KGGTGGAAGLLGWGANGKKRGAVTGPPSPVAAQENKKTVQRQAGCTNDVTINPKKVVANVYTVQR
QAGCTNKKGGVGTTGGAGGNGGGAKKGNSGTANTGFGNAGNVKKTATGIWYLQDRVIVAEKKISTY
FSALLADPTTTPKKNAGAGNTGFFDAGNYNGPGPGYAVAEAATAQSVQQDGPGPGGQEYQAVSAQ
ASAFHGPGPGGNSYAVAEAATAQSVGPGPGRKDIRTTRVTVAPQYGPGPGGGNSYAVAEAATAQS
AAYSTQAKTRAMAAYFMLIGAAFYAAYSGYSGGLYYAAYQAALAMEVYAAYNFMLIGAAFAAYRTYE
ATMSLAAYAAAASTTALAAYYVNTSTTSM 
 

 
Adjuvant (RPFE) =1-172 

 
14 B-cell epitopes =178-250 

 
5 MHC II (HTL epitopes) = 433-527 

 
5 MHC I (CTL epitopes) = 530-587 

 
3 MHC I (HLA-C epitopes) = 590-623 

 
Linkers 

 
Table 1. Comprehensive Epitope Mapping of Mycobacterium tuberculosis Antigens for Multi-

Epitope Vaccine Design 
 

Gene Name B-Cell Epitope CTL Epitope HLA-C Epitope HTL Epitope 

PPE 68  STQAKTRAM  
(B7,B8,B62) 

  

QAALAMEVY  
(A1,A26,B58,B62) 

Rv0310c TATGIWYLQDRVIVAE 
 

FMLIGAAFY  
(A1,A3,A26,B62) 

RTYEATMSL 
(HLA-C*01:02 
HLA-C*03:02 
HLA-C*03:03 
HLA-C*03:04 
HLA-C*04:01 
HLA-C*05:01 
HLA-C*07:01 
HLA-C*07:02 
HLA-C*07:04 
HLA-C*08:01 
HLA-C*08:02 
HLA-C*12:02 
HLA-C*12:03 
HLA-C*15:02 
HLA-C*16:01 
HLA-C*17:01) 

 

NFMLIGAAF  
(A24,B8,B62) 

PE_PGRS35 AGLIGNGGAGGAGGPN SGYSGGLYY  
(A3,A26,B58,B62) 

 GQEYQAVSAQASAFH 
(DRB1_0101 
DRB1_0401 
DRB1_0405 
DRB1_0701 
DRB1_0802 
DRB1_0901 
DRB1_1101 
DRB3_0202 

GSPITTLYVKIDGGTP 

GGTGGAAGLLGWGANG 

ISTYFSALLADPTTTP 



 
 
 
 

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Gene Name B-Cell Epitope CTL Epitope HLA-C Epitope HTL Epitope 

HLA-DQA10501-
DQB10301 
HLA-DQA10301-
DQB10302 
HLA-DQA10401-
DQB10402 
HLA-DQA10102-
DQB10602 
HLA-DPA10201-
DPB11401) 

PE_PGRS31 AGLWGNGGSGGAGGNA 
GGMGGSGGGIGAGTTT 
 

 AAAASTTAL 
(HLA-C*01:02 
HLA-C*03:02 
HLA-C*03:03 
HLA-C*03:04 
HLA-C*05:01 
HLA-C*07:04 
HLA-C*08:01 
HLA-C*08:02 
HLA-C*12:02 
HLA-C*15:02 
HLA-C*16:01 
HLA-C*17:01) 

YAVAEAATAQSVQQD 
(HLA-DQA10501-
DQB10201 
HLA-DQA10501-
DQB10301 
HLA-DQA10301-
DQB10302 
HLA-DQA10401-
DQB10402 
HLA-DQA10102-
DQB10602) 

GNSYAVAEAATAQSV 
(DRB1_0101 
DRB1_1101 
DRB3_0202 
HLA-DQA10501-
DQB10301 
HLA-DPA10201-
DPB11401) 

GGVGTTGGAGGNGGGA 

GGNSYAVAEAATAQS 
(DRB1_0405 
DRB1_0701 
DRB1_0802 
DRB1_0901 
HLA-DQA10501-
DQB10201 
HLA-DQA10301-
DQB10302 
HLA-DQA10401-
DQB10402) 

lpqG    RKDIRTTRVTVAPQY 
(DRB1_0401 
DRB1_0701 
DRB1_0802 
DRB4_0101 
HLA-DQA10301-
DQB10302 
HLA-DQA10401-
DQB10402 
HLA-DQA10102-
DQB10602 
HLA-DPA10201-
DPB11401) 

PPE39 GNSGTANTGFGNAGNV  YVNTSTTSM 
(HLA-C*02:02 
HLA-C*02:09 
HLA-C*03:02 
HLA-C*03:03 
HLA-C*03:04 
HLA-C*08:02 
HLA-C*12:02 
HLA-C*12:03 

 

NAGAGNTGFFDAGNYN 



 
 
 
 

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Gene Name B-Cell Epitope CTL Epitope HLA-C Epitope HTL Epitope 

HLA-C*15:02 
HLA-C*16:01 
HLA-C*17:01) 

Rv1975 NVPIDPSPDYDASDEI    

RGAVTGPPSPVAAQEN 

TVQRQAGCTNDVTINP 

VVANVYTVQRQAGCTN 

 
Table 2. Docking results (PyDock) Vaccine construct with TLR4 

 

RMSD from the overall lowest-energy structure 0.248 
Van der Waals energy 42.436 
Electrostatic energy -10.009 
Desolvation energy -49.937 
Z-Score -1.10 

 
Table 3. Vaccine construct with MHC I 

 

RMSD from the overall lowest-energy structure 0.248 
Van der Waals energy 81.423 
Electrostatic energy -19.16 
Desolvation energy -32.681 
Z-Score -2.89 

 
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