Development of a multi-epitope vaccine against Mycobacterium tuberculosis using an immunoinformatics approach

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Given the limited efficacy of the licensed BCG vaccine against adult pulmonary TB and latent infection reactivation, novel vaccines are imperative. This study designed an immunoinformatics-driven multi-epitope vaccine by screening target proteins for B-cell, cytotoxic T-lymphocyte (CTL), and helper T-lymphocyte (HTL) epitopes, fused with RpfE adjuvant. The construct demonstrated antigenicity, solubility, and non-allergenicity in physicochemical analyses. Molecular docking, dynamics simulations, and immune modeling predicted robust immune responses, while in silico cloning confirmed feasibility. Though promising as a TB vaccine candidate, in vivo validation of protective efficacy remains essential. Biological sciences/Biotechnology Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Immunology Biological sciences/Microbiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Mycobacterium tuberculosis (MTB) is a significant airborne infectious agent in humans, causing tuberculosis (TB), highly contagious and has infected approximately one-third of the world's human population [1]. Tuberculosis (TB) is the leading global infectious disease killer [2], claiming 1.25 million lives just in 2023 [3], representing a substantial economic burden of disease across multiple countries [4], which poses an important threat to global public health [5]. According to the latest World Health Organization (WHO) report, among the estimated 10.8 million people who developed tuberculosis (TB) in 2023, males accounted for 55%, females 33%, and children 12% [3]. Since 2020, the incidence rate of Mycobacterium tuberculosis (TB) infection has risen by 4.6% [3]. Despite advances in TB diagnostic and therapeutic approaches, a substantial number of patients continue to be affected, making it the world's second deadliest infectious disease [6]. The Bacillus Calmette-Guérin (BCG) vaccine, a live attenuated strain, has been the sole licensed tuberculosis vaccine since 1923 and is administered intradermally to prevent pulmonary TB [5]. Clinical evidence, however, reveals substantial heterogeneity in BCG's protective effectiveness against pulmonary TB across trial populations. Overall, global efficacy exhibits high variability due to factors including genetic divergence in BCG strains, host population heterogeneity, interference from non-tuberculous mycobacteria (NTM), and concurrent parasitic infections [7]. This inconsistent protection contributes to persistently high mortality and transmission rates [8,9]. While conventional BCG vaccination effectively prevents pediatric pulmonary TB, it demonstrates limited efficacy against adult pulmonary tuberculosis. Moreover, this live-attenuated vaccine poses significant virulence reversion risks when administered to immunocompromised individuals [10]. To date, only a limited number of countries incorporate BCG into routine immunization programs, while others have adopted targeted vaccination strategies following BCG-associated fatalities in immunocompromised children and documented adverse events [11]. Consequently, these multifaceted challenges significantly impede global tuberculosis (TB) control programs developing rapidly. Currently, multiple tuberculosis vaccines are in various stages of clinical trials; nevertheless, substantial safety concerns persist—nearly 50% of candidate vaccines employ live attenuated Mycobacterium tuberculosis strains [12]. While viral-vectored vaccines (Crucell-Ad35, MVA85A) achieved only partial protection [13], recombinant candidates VPM1002/MTBVAC present virulence restoration concerns [14]. Subunit formulations like M72/H4 suffer from weak immunogenicity demanding repeated boosting [15]. The M72/AS01E vaccine, despite promising early results, induced localized adverse events at injection sites during Phase II evaluation [16]. Whereas, clinical trial data demonstrate considerable promise for multi-epitope subunit vaccines in combating tuberculosis [17,18]. Several research teams have employed immunoinformatics approaches to develop TB multi-epitope vaccines [19,20]. For instance, Jiang et al. recently designed the C624P multi-epitope vaccine against Mycobacterium tuberculosis through immunoinformatics analysis, which candidate exhibits exceptional antigenicity, immunogenicity, structural stability, and immune activation potential for latent TB infection (LTBI) prevention [21], reflecting growing momentum in TB peptide vaccine development. This study designed a computationally engineered multi-epitope peptide vaccine by integrating the most antigenic, non-allergenic, and non-toxic epitopes derived from four Mycobacterium tuberculosis (Mtb) antigenic proteins. Computational tools screened protein sequences to identify optimal B-cell, helper T-lymphocyte (HTL), and cytotoxic T-lymphocyte (CTL) epitopes. All candidate epitopes underwent rigorous antigenicity, allergenicity, and toxicity assessments prior to vaccine construction. The epitope linkage strategy was optimized to enhance structural flexibility and protein folding efficiency, addressing the global demand for more effective TB vaccines. Furthermore, the predicted immunological characteristics demonstrate vaccine immunogenicity and establish a theoretical foundation for subsequent experimental validation. 2 Materials and methods 2.1 Retrieving Protein Sequences The NCBI (National Center for Biotechnology Information) protein database [22] was utilized to search for target proteins. Subsequently, proteins were retrieved from Mycobacterium tuberculosis strain ATCC H37Rv: PPE family protein PPE18 ( NCBI ID: YP_177795.1), lipoprotein LpqG ( NCBI ID: NP_218140.1), ESAT-6 like protein EsxW (NCBI ID: NP_218137.1), and serine/threonine-protein kinase PknB (NCBI ID: NP_214528.1), obtaining the amino acid sequence of the adjuvant RpfE (Rv2450c). These data were acquired in FASTA format for integrated downstream analysis. The antigenic propensity of each selected protein was predicted using the antigenic peptide prediction tool [23]. Simultaneously, allergenic properties were identified via the descriptor-based fingerprinting server AllergenFP v.1.0[24]. To mitigate autoimmunity risks, all selected proteins underwent BLASTp screening against the human proteome with default parameters [25]. The multi-epitope vaccine design workflow is illustrated in Fig. 1 . 2.2 Epitope Prediction 2.2.1 B-Cell Epitope Prediction B-cell epitope prediction is crucial in vaccine design. Consequently, this study employed the ABCpred server for prediction [26]. ABCpred is an artificial neural network-based online server designed to identify linear B-cell epitopes within antigenic proteins. Amino acid sequences of selected proteins were submitted in single-letter code format without header lines, employing a threshold score of 0.51 with 16-mer epitope length, keeping on overlapping filter, and epitopes scoring > 0.8 were selected as optimal candidates for downstream analysis. 2.2.2 Helper T Lymphocyte (HTL) Epitope Prediction HTL epitopes of the chosen proteins were predicted using the IEDB MHC-II server [27]. The Immune Epitope Database and Analysis Resource (IEDB) is an online database and tool suite for predicting immune epitopes from experimentally validated collections [28]. Amino acid sequences of selected proteins were independently submitted in FASTA format. HTL epitope prediction was performed using default system parameters, with epitope length set to 15-mer, using the complete HLA human reference set for epitope prediction, and the resulting epitopes were ranked according to adjusted rank score with the lowest percentile rank were selected for further study. 2.2.3 Cytotoxic T Lymphocyte (CTL) Epitope Prediction CTL epitopes were predicted using the IEDB MHC-I server [8]. Amino acid sequences of selected proteins were submitted in FASTA format. Employing the complete human HLA reference set, predictions used default IEDB recommended settings (2023.09 version, NetMHCpan EL 4.1) for 9-mer and 10-mer epitopes. Results were sorted by prediction score. Redundant HTL and CTL epitopes targeting identical HLA alleles were eliminated. Selected epitopes underwent further evaluation for antigenicity, allergenicity and toxicity. 2.2.4 Epitope Antigenicity, Allergenicity, and Toxicity Prediction All selected epitopes underwent evaluation for antigenicity, allergenicity, and toxicity. Antigenicity prediction used the VaxiJen v2.0 web server [29], and bacterial threshold: 0.4, which employs physicochemical properties to predict antigenicity through alignment-independent approaches. Allergenicity assessment utilized AllerTop v2.0 [30], implementing an auto-cross covariance (ACC) algorithm based on. Toxicity prediction used the ToxinPred server [31], with only antigenic, non-allergenic, and non-toxic epitopes selected for vaccine construction. 2.3 Vaccine Construction 2.3.1 Subunit Vaccine Construction The previously predicted epitopes and linker peptides were combined to construct a subunit vaccine. The B-cell, HTL, and CTL epitopes with the highest scores for antigenicity, non-allergenicity, and non-toxicity were linked via KK, GPGPG, and AAY, respectively. The amino acid sequence of the RpfE (Rv2450c) adjuvant was connected to the N-terminus of the vaccine construct via EAAAK. To screen for the optimal vaccine candidate, the antigenicity of the vaccine constructs was evaluated using VaxiJen and AntigenPro software, while their allergenicity was assessed using AllerTop V. 2.1. 2.3.2 Model Vaccine Homology Construction To validate the structural stability of the selected protein, homology modeling of the vaccine construct was performed using web servers including I-TASSER [32], SWISS-MODEL [33], and PHYRE2 [34]. The generated prediction structural models were downloaded in PDB format to enable downstream analysis and validation procedures. 2.3.3 Candidate Vaccine Structural validation Tertiary structure prediction was performed via AlphaFold, enabling identification of conformationally optimal epitope placements [35], employing Chimera version 1.17.1 for 3D structural visualization [36]. Secondary structure elements (α-helices, β-strands, and coils) were subsequently profiled using PSIPREDv4.0 and GORIV web servers, which implement complementary neural network and information-theory frameworks [37,38]. 2.3.4 Vaccine Construction Physicochemical characterization In silico analysis of physicochemical characteristics was performed via Expasy's ProtParam web server tool, key parameters including protein length, isoelectric point (pI), molecular weight, half-life, instability index, aliphatic index, and grand average of hydropathy (GRAVY)[39] . 2.3.5 Vaccine Construction Molecular docking and dynamic simulation The vaccine construct (antigen) was computationally docked against immune receptors TLR2 and TLR4 via the ClusPro 2.0 web server [40], which implements fast Fourier transform-based correlation approaches for binding pose prediction. Dominant binding conformations were rendered using UCSF Chimera version 1.17.1 for structural interrogation [41]. To evaluate the stability of the docked complexes under physiological conditions, molecular dynamics simulations (MDS) of the vaccine-TLR2 and vaccine-TLR4 complexes were performed using GROMACS 5.0 [42]. This analysis assessed structural integrity by monitoring four key parameters: root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA). 2.4 Vaccine Population coverage analysis Given the heterogeneity in human leukocyte antigen (HLA) allele frequencies across global populations, the protective efficacy of the designed vaccine was evaluated using the IEDB Population Coverage Calculation tool with default parameters to predict population-level immunoresponsiveness [43]. 2.5 Codon optimization and cloning The vaccine coding sequence was optimized for Escherichia coli expression using JCat, which maximizes translation efficiency through codon usage adaptation. Key optimization metrics, including GC content and Codon Adaptation Index (CAI) were recorded [44]. The synthetic gene was flanked by Xho1 and BamH1 NLS restriction sites at its termini and directionally cloned into the multiple cloning site of pET28a (+) vector, confirming construct fidelity via SnapGene tool. 2.6 Immunogenicity assessment The immunogenicity response dynamics of our vaccine construct, in silico immune simulation was performed using the C-ImmSim server under default parameter settings [45], mimicking natural immune responses in humans. This tool agent-based immune simulator employs position-specific scoring matrices (PSSMs) coupled with machine learning algorithms to evaluate immunogenicity. 3 Results 3.1 Protein sequence retrieval This study retrieved four immunogenic proteins (ATCC H37Rv: PPE family protein PPE18 ( NCBI ID: YP_177795.1, length:391 aa ), lipoprotein LpqG ( NCBI ID: NP_218140.1, length:240 aa ), ESAT-6 like protein EsxW (NCBI ID: NP_218137.1, length:98 aa), and serine/threonine-protein kinase PknB (NCBI ID: NP_214528.1, length:626 aa). ) of the Mycobacterium tuberculosis H37Rv strain from the NCBI (National Center for Biotechnology Information) database and conducted immunogenicity prediction and physicochemical property analysis to construct a vaccine. 3.2 Epitope Prediction 3.2.1 B-Cell Epitope Prediction Epitopes with scores greater than 0.8 predicted by the ABCpred web server were selected for further analysis of their antigenicity, allergenicity, and toxicity. Duplicate epitopes identified across different HLA groups were excluded from the study, and epitopes predicted to be non-antigenic, allergenic, or toxic by VaxiJen, AllerTop v.2.0, and ToxinPred, respectively, were also removed. Ultimately, 26 B-cell epitopes were identified for the four selected proteins (Table 1 ). Table 1 Prediction of B-cell epitopes for the input MTB protein sequences. Rank Protein name Sequence Start position Sore 1 PPE18 AGPGSASLVAAAQMWD 18 0.84 VRVAAAAYETAYGLTV 91 0.83 GSSLGSSGLGGGVAAN 293 0.83 FGYAAATATATATLLP 153 0.83 2 LPQG VAPQYSNPEPAGTATI 101 0.88 RKDIRTTRVTVAPQYS 91 0.86 DALVGAGLDRKDIRTT 82 0.85 LSGCDSHNSGSLGADP 20 0.84 3 ExsW QNISGAGWSGMAEATS 38 0.86 LHGVRDGLVRDANNYE 71 0.84 4 PknB AGEVTGTNPPAGTTVP 531 0.93 SGPATKDIPDVAGQTV 490 0.91 TGMLDKGADVDAGGSQ 584 0.9 LQKPDSTIPPDHVIGT 387 0.88 GEPPFTGDSPVSVAYQ 212 0.88 DVDAGGSQHNRVVYQN 592 0.86 PYIVMEYVDGVTLRDI 88 0.85 ARDPSFYLRFRREAQN 47 0.85 LLSSAAGNLSGPRTDP 296 0.85 ENRYQTAAEMRADLVR 260 0.85 GGMSEVHLARDLRLHR 20 0.85 IGTAQYLSPEQARGDS 177 0.85 EQARGDSVDARSDVYS 186 0.83 NGIIHRDVKPANIMIS 132 0.82 TGEAETPAGPLPYIVM 77 0.81 3.2.2 Helper T Lymphocyte (HTL) Epitope Prediction Based on the corresponding four proteins, we selected seven HTL (helper T lymphocyte) epitopes with strong antigenicity, non-allergenic properties, and non-toxicity for vaccine construction (Table 2 ). Table 2 Prediction of HTL epitopes for the input MTB protein sequences. Si.No Protein name Allele Sequence Start position Sore 1 PPE18 HLA-DQA1*01:02/DQB1*06:02 AELTAAQVRVAAAAY 84 0.8876 HLA-DQA1*01:02/DQB1*06:02 AGQAELTAAQVRVAA 81 0.899 HLA-DQA1*01:02/DQB1*06:02 GQAELTAAQVRVAAA 82 0.9304 HLA-DQA1*01:02/DQB1*06:02 QAELTAAQVRVAAAA 83 0.9391 2 LPQG HLA-DRB1*01:01 RADQYAQLSGLRLGK 180 0.992 3 PknB HLA-DRB4*01:01 PDVAGQTVDVAQKNL 498 0.9519 HLA-DRB4*01:01 IPDVAGQTVDVAQKN 497 0.9696 3.2.3 Cytotoxic T Lymphocyte (CTL) Epitope Prediction According to the highest binding affinity to MHC I molecules, epitopes were screened and then ranked according to their percentile scores. Those in the lowest percentile rank were selected for antigenicity, allergenicity, and toxicity analyses. Duplicate CTL epitopes across different HLA combinations were excluded from the study, and candidate epitopes demonstrating antigenicity, non-allergenicity, and non-toxicity were chosen for further investigation. Ultimately, six CTL epitopes were identified for the PPE18, LPQG, EsxW, and PknB proteins, whereas no epitopes meeting the preliminary criteria were found for the PPE18 protein (Table 3 ). Table 3 Prediction of CTL epitopes for the input MTB protein sequences. Si.No Protein name Allele Sequence Start position Sore 1 LPQG HLA-B*57:01 VGFSVTVVW 229 0.93074 2 ExsW HLA-B*40:01 QEQASQQIL 88 0.971951 HLA-B*44:02 TEDEARRMW 25 0.953543 3 PknB HLA-B*40:01 REAQNAL 58 0.988123 HLA-B*40:01 MEYVDGVTL 92 0.986757 HLA-A*11:01 VIELQVSK 549 0.97906 3.3 Vaccine Construction 3.3.1 Subunit Vaccine Construction Through stochastic rearrangement of selected epitope positions ordered sequentially as B-cell, HTL, and CTL epitopes, a randomized vaccine construct was generated. Concurrently, B-cell, HTL, and CTL epitopes were connected via KK, GPGPG, and AAY linkers, respectively. The RpfE adjuvant protein sequence was fused to the N-terminus of the vaccine construct using an EAAAK linker, and designed a 748 amino acid linear vaccine construct (Fig. 2 ). Subsequent antigenicity and allergenicity predictions confirmed the construct's antigenic and non-allergenic properties. 3.3.2 Vaccine Homology and Structural Validation To ensure the structural stability of the selected protein, we performed homology modeling of the vaccine construct using web servers such as I-TASSER, SWISS-MODEL, and PHYRE2. Subsequently, secondary structure elements were analyzed using PSIPRED v4.0 and GOR IV web servers (Fig. 3 A), revealing an Alpha helix (Hh) content of 42.65%, Extended strand (Ee) of 10.56%, and Random coil (Cc) of 46.79%, while all other metrics 3 10 helix (Gg), Pi helix (Ii), Beta bridge (Bb), Beta turn (Tt), Bend region (Ss), Ambiguous states, and Other states were 0. Tertiary structure prediction was performed using PHYRE2.0 to identify conformationally optimal epitope positions, and 3D structure visualization was conducted with SWISS-MODEL software (Fig. 3 B). The generated homology model exhibited optimal Ramachandran plot values. Structural refinement was subsequently carried out using GalaxyRefine (Fig. 3 C). Among the five models generated by GalaxyRefine, Model 5 (Fig. 3 C) demonstrated the most favorable metrics: a GDT-HA score of 0.971, RMSD value of 0.364, and MolProbity score of 1.837. After refinement, the percentage of residues in the most favored regions of the Ramachandran plot increased to 97.10%, showing significant improvement over the initial structure generated by SWISS-MODEL (Fig. 3 A). Based on these results, the optimized Model 5 was ultimately selected for molecular docking and dynamics simulation studies. 3.3.3 Vaccine Construction Physicochemical characterization Computer-simulated analysis of the physicochemical properties of the vaccine was performed using the Expasy ProtParam web server tool. The protein length, isoelectric point (pI), molecular weight, half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) were 748, 9.73, 77869.81, 4.4 hours, 25.74, 70.57, and − 0.475 respectively. 3.3.4 Vaccine Construction Molecular Docking and Dynamics Simulation Molecular dynamics simulation analysis reveals that the vaccine-receptor complex undergoes significant structural rearrangement during the initial 100 ps (RMSD increased from 0.10 nm to 0.20 nm, Rg decreased from 2.05 nm to 1.95 nm, SASA reduced from 155 nm² to 150 nm²) before reaching a stable state (RMSD stabilized at 0.15 nm < 0.2 nm threshold, Rg ≈ 1.98 nm ± 0.02 nm, SASA ≈ 150 nm² ± 2 nm²), confirming excellent structural stability. Simultaneously, RMSF analysis identifies high-flexibility sites in residue regions 20–40, 80–100, 140–160, and 180–200 (peaking at ~ 0.25 nm), where these potential functional active zones and rigid structures (residues 50–70, 110–130) collectively maintain the complex's dynamic equilibrium (Fig. 4 ), providing crucial molecular mechanism insights for optimizing candidate vaccine design. 3.4 Vaccine Population coverage analysis Population coverage analysis was conducted using the vaccine and its corresponding human leukocyte antigen (HLA) alleles to predict immune responses across diverse global populations. The study revealed that this vaccine achieves a global population average coverage of 88.22%. North America exhibited the highest coverage (100.0%), followed by Europe (99.92%) and West Africa (99.4%), while the lowest coverage was observed in South Africa (18.36%). Moreover, East Asia, Northeast Asia, Southeast Asia, Southwest Asia, East Africa, Central Africa, North Africa, West Indies, Central America, South America, Oceania were 89.24,96.11,99.0,81.05,79.39,96.48,95.56,77.17,89.3,92.54,99.39 and 98.61 respectively (Fig. 5 ), and information concerning regional divisions is presented in the supplementary materials. Therefore, the vaccine is expected to elicit efficacy across populations with diverse genetic backgrounds. 3.5 Codon optimization and cloning Maximizing vaccine expression levels in experimental research is particularly critical, where codon optimization of the amino acid sequence depends on the host expression system. This vaccine was codon-optimized for the E. coli expression system, resulting in a final sequence of 748 amino acids (2244bp). The Codon Adaptation Index (CAI) and GC content of the vaccine were 0.92 and 68%, respectively, both within the optimal range. Furthermore, the Xho1 and BamH1 restriction enzymes were selected and added to the N- and C-termini of the vaccine sequence, respectively. The cloning process of inserting the cDNA into the pET28a (+) vector was simulated in silico using the SnapGene tool (Fig. 6 ). 3.6 Immunogenicity assessment The immune simulation results generated by the C-ImmSim server indicated alignment with a potent immune response. As illustrated in the figures, our vaccine construct elicited robust primary and secondary immune responses. The primary immune response was characterized by elevated IgM antibody levels following a 15–20 day latent period after antigen exposure. The secondary immune response exhibited accelerated B-cell proliferation and increased expression of IgM, IgG1 + IgG2, and IgG + IgM antibodies. This vaccine construct not only effectively stimulated B-cell proliferation but also induced the generation of memory B cells. It simultaneously triggered a strong helper T-cell and cytotoxic T-cell immune response (Fig. 7 ). Discussion Mycobacterium tuberculosis (MTB) is a significant contributor to global mortality, posing a grave threat to global human health. Although multiple anti-tuberculosis drugs are commercially available, their therapeutic efficacy remains limited, while the pathogen has developed substantial drug resistance, rendering its multidrug-resistant properties well-documented. Consequently, preventing drug-resistant tuberculosis stands as a critical priority for global health efforts [46]. Bacillus Calmette–Guérin (BCG) remains the sole available tuberculosis preventive vaccine [47]. While it demonstrates proven efficacy against childhood extrapulmonary TB, its protective effect against adult tuberculosis is minimal to nonexistent. Variations in BCG efficacy are attributable to genetic divergence among BCG strains resulting from differential culturing protocols across laboratories [48]. Concurrently, prior exposure to environmental mycobacteria and the induction of short-lived T effector memory cells contribute to BCG's failure to fully prevent mycobacterial infection, leading to BCG's genomic deletions in RD (Region of Difference) regions and absence of several key protective antigens [49]. It is well established that vaccine development constitutes an extensive and complex process, demanding not only substantial financial investment and time commitment, but also rigorous laboratory validation of safety and efficacy. However, with advances in new-generation information technologies and interdisciplinary convergence, immunoinformatics has pioneered novel methodologies for pharmaceutical and vaccine research, propelling the field into an accelerated development trajectory. Immunoinformatic approaches have been repeatedly employed to develop vaccines against diverse pathogens, demonstrating compelling efficacy outcomes. Several candidates are now in late-stage clinical development, including those targeting salmonella enterica, streptococcus pyogenes, brucella spp, staphylococcus aureus, and dengue virus [50–54]. However, prior studies by Shiraz et al. and Sharma et al.primarily focused on epitope-based vaccine design through antigen selection without conducting population coverage analysis [8,55]. In contrast, our study incorporates comprehensive population coverage assessment, yielding critical insights into vaccine applicability across human populations. To address the urgent global need for effective tuberculosis interventions, we designed a multiepitope vaccine incorporating four immunogenic antigens. Immunogenicity, homology, allergenicity, and toxicity of each antigen were assessed through independent predictions of B-cell (BCL), helper T-cell (HTL), and cytotoxic T-lymphocyte (CTL) epitopes. Selected epitopes were subsequently assembled via linker peptides into a candidate vaccine construct, ensuring proper protein folding and structural flexibility. The construct integrates adjuvant RpfE (Rv2450c) conjugated to epitopes using EAAAK linkers to form a three-dimensional architecture [56]. Epitope-specific linkers were strategically implemented: GPGPG spacers induced HTL responses while minimizing junctional immunogenicity [57]; AAY (Ala-Ala-Tyr) linkers enhanced vaccine immunogenicity by reducing neo-epitope formation [58]; KK dipeptides optimized overall immunogenic potential [59]. To potentiate immune activation, RpfE adjuvant was tethered to the N-terminal epitopes via EAAAK linkers. The resulting construct demonstrated favorable biosafety profiles (non-allergenic, non-toxic), strong antigenicity, and 88.22% global population coverage. Further codon optimization and in silico cloning yielded ideal codon adaptation index (CAI = 0.92) and GC content (60%). Finally, tertiary structure modeling with validation through Ramachandran plot analysis (91.7% favored regions), and RMSD (0.364) confirmed conformational stability, indicating this vaccine elicits robust immune responses against Mycobacterium tuberculosis (MTB), establishing a novel strategic framework for global tuberculosis prevention and control. Therefore, its implementation will offer significant safeguards for advancing global health equity and maximizing population health rights and interests. Conclusion This study selected four Mycobacterium tuberculosis (MTB) antigenic proteins to construct a multiepitope TB vaccine. Using diverse immunoinformatic approaches, we identified immunodominant and non-allergenic peptides from these antigens. Preliminary assessment of the candidate vaccine's structural, immunological, and physicochemical characteristics suggests its potential to elicit cell-mediated immune responses against MTB, positioning it as a lead vaccine candidate. Subsequent in vitro and in vivo evaluations will be conducted, utilizing animal models to validate the vaccine's expression profile and immunogenic potency to further determine its safety and protective efficacy. Declarations Acknowledgements Nothing to declare. Author contributions WF and SZY conceived the study. FLJ，WXP contributed to study design/supervision. WF and CMB conducted data integration/analysis. The manuscript was drafted by WF and SZY, revised by all authors, and finalized with critical input from WF. All authors reviewed and approved the final version. Fundings This work was supported by the Ningxia Natural Science Foundation Project (NO: 2023AAC03516); State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (NO: SKL-HIDCA-2024-35). Data Availability All data generated or examined throughout this research has been included in this published article and its supplementary information files. 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Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999;112:531–552. Kozakov D, Hall DR, Xia B, et al. The ClusPro web server for protein-protein docking. Nat Protoc. 2017;12(2):255–278. Pettersen EF, Goddard TD, Huang CC, et al. UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem. 2004;25(13):1605–1612. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J Comput Chem. 2005;26(16):1701–1718. Bui HH, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics. 2006;7:153. Grote A, Hiller K, Scheer M, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 2005;33 (Web Server issue): W526-W531. Rapin N, Lund O, Bernaschi M, Castiglione F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS One. 2010;5(4):e9862. Fox GJ, Nhung NV, Cam Binh N, et al. Levofloxacin for the Prevention of Multidrug-Resistant Tuberculosis in Vietnam. N Engl J Med. 2024;391(24):2304–2314. Trunz BB, Fine P, Dye C. Effect of BCG vaccination on childhood tuberculous meningitis and miliary tuberculosis worldwide: a meta-analysis and assessment of cost-effectiveness. Lancet. 2006;367(9517):1173–1180. Mangtani P, Abubakar I, Ariti C, et al. Protection by BCG vaccine against tuberculosis: a systematic review of randomized controlled trials. Clin Infect Dis. 2014;58(4):470–480. Ansari MA, Zubair S, Mahmood A, et al. RD antigen based nanovaccine imparts long term protection by inducing memory response against experimental murine tuberculosis. PLoS One. 2011;6(8):e22889. Jafari Najaf Abadi MH, Abdi Abyaneh F, Zare N, et al. In silico design and immunoinformatics analysis of a chimeric vaccine construct based on Salmonella pathogenesis factors. Microb Pathog. 2023;180:106130. Aslam S, Ashfaq UA, Zia T, et al. Proteome based mapping and reverse vaccinology techniques to contrive multi-epitope based subunit vaccine (MEBSV) against Streptococcus pyogenes. Infect Genet Evol. 2022;100:105259. Li M, Zhu Y, Niu C, et al. Design of a multi-epitope vaccine candidate against Brucella melitensis. Sci Rep. 2022;12(1):10146. Tahir Ul Qamar M, Ahmad S, Fatima I, et al. Designing multi-epitope vaccine against Staphylococcus aureus by employing subtractive proteomics, reverse vaccinology and immuno-informatics approaches. Comput Biol Med. 2021;132:104389. Fadaka AO, Sibuyi NRS, Martin DR, et al. Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus. Sci Rep. 2021;11(1):19707. Shiraz M, Lata S, Kumar P, Shankar UN, Akif M. Immunoinformatics analysis of antigenic epitopes and designing of a multi-epitope peptide vaccine from putative nitro-reductases of Mycobacterium tuberculosis DosR. Infect Genet Evol. 2021;94:105017. Chen X, Zaro JL, Shen WC. Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev. 2013;65(10):1357–1369. Livingston B, Crimi C, Newman M, et al. A rational strategy to design multiepitope immunogens based on multiple Th lymphocyte epitopes. J Immunol. 2002;168(11):5499–5506. Yang Y, Sun W, Guo J, et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum Vaccin Immunother. 2015;11(3):795–805. Yano A, Onozuka A, Asahi-Ozaki Y, et al. An ingenious design for peptide vaccines. Vaccine. 2005;23(17–18):2322–2326. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7214810","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":500267333,"identity":"e1e242a0-6c87-4957-a32d-f868fc7d149a","order_by":0,"name":"Fan Wang","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Wang","suffix":""},{"id":500267334,"identity":"8434b4b1-8217-482e-b135-49349a8007f5","order_by":1,"name":"Mingbo Chen","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingbo","middleName":"","lastName":"Chen","suffix":""},{"id":500267335,"identity":"4e1bbe14-4248-498d-96f3-a79929074abd","order_by":2,"name":"Lijun Feng","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Feng","suffix":""},{"id":500267336,"identity":"df576516-123f-402c-8ba4-39fe45c8e718","order_by":3,"name":"Xiaoping Wang","email":"","orcid":"","institution":"The Fourth People's Hospital of Ningxia Hui Autonomous Region","correspondingAuthor":false,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Wang","suffix":""},{"id":500267337,"identity":"e956146b-eac5-494d-9a38-dfe1ff09ce10","order_by":4,"name":"Zhiyun Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PsQrCMBCA4ZNAugRcC0X7CicBEezDNAh1ESm4SySrD1DwJbqJm3Lo6trBQfAFhC4OglY7uLV1E8w/3ZDvkgDYbL+Y29IMsBgYo/MVg+4XxOFRL4kj2YAU+99DW6AnrqRqhb8yJo9jUuviLhkgC8GhfVpF8LTTMkFSGwPhZYJ8CiKKskriKj0SBUkJtnKCYgau6FcSP1GaStLS3uC1oY5AphamJAw8QKwnmCnDBI5lSpz3lhhKXvcXPxlfcnEfdtLjMT/f7o9u26FD9cPKzGfkDY6/mjc8Z7PZbH/ZE/WqSkoPMLhKAAAAAElFTkSuQmCC","orcid":"","institution":"General Hospital of Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhiyun","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2025-07-25 13:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7214810/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7214810/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89419839,"identity":"75baf6aa-9e85-4720-a241-10a17e2a3389","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2197661,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow Diagram for Developing Vaccine\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/4285b202e119f15c5eb6f912.jpg"},{"id":89419894,"identity":"e6b05144-68b6-4f27-bf34-ea1eb2960f72","added_by":"auto","created_at":"2025-08-19 18:18:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2755340,"visible":true,"origin":"","legend":"\u003cp\u003eA shows a schematic diagram of the final vaccine construct. B displays the main sequence of the vaccine construct, where the B-cell, CTL, and HTL epitopes are shown in purple, blue, and green, respectively. The vaccine adjuvant (pink) is attached to the N-terminus of the sequence via an EAAAK linker (in red). The B-cell, HTL, and CTL epitopes are connected by KK (purple), GPGPG (blue), and AAY (green) linkers respectively.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/e29bc1e4b2c504496be24952.jpg"},{"id":89419845,"identity":"4d4b9303-885b-4e3d-94b0-c5f4654dfc39","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1338100,"visible":true,"origin":"","legend":"\u003cp\u003eShowing the structural diagram of the vaccine. A indicates the secondary structure of the vaccine, B indicates the tertiary structure of the vaccine, and C indicates the optimized tertiary structure of the vaccine.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/5902880612a553aab6f5a3cf.jpg"},{"id":89419843,"identity":"b6f55a37-b161-4143-8db4-f51d1c92f4f2","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1155044,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking and dynamics simulation\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/32aa3a97fe2f4485b5193876.jpg"},{"id":89419846,"identity":"c16b398b-7ff3-4408-876e-864c28b6e9b5","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1409005,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal vaccine population coverage\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/342fcf3780e7e1f2024bce92.jpg"},{"id":89419848,"identity":"413aa306-d29e-4e11-8582-466858948d44","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1236720,"visible":true,"origin":"","legend":"\u003cp\u003eCodon optimization and cloning. The vaccine design was inserted into the pET28a (+) expression vector through Xho1 and BamH1 restriction sites in purple. The vaccine insertion region is highlighted in red, while the vector pET28a (+) is indicated in black.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/674f332efaa934a6fcf2c964.jpg"},{"id":89419852,"identity":"80590892-d587-4868-b41d-17ef2d456217","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1316930,"visible":true,"origin":"","legend":"\u003cp\u003eImmunogenicity assessment. A indicates B-Cell population dynamics; B indicates antibody isotype expression; C indicates T-Cell state dynamics, and D indicates immune response comparison.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/a9c3c762f9d7de6738346070.jpg"},{"id":103890575,"identity":"e2370fb6-3b50-4b3b-8242-319be675ca06","added_by":"auto","created_at":"2026-03-04 07:58:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12552796,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/42cd5002-1e7d-4fe4-a04b-483f586accbd.pdf"},{"id":89419842,"identity":"e7b223c4-7b36-4526-9200-da354800710e","added_by":"auto","created_at":"2025-08-19 18:18:06","extension":"doc","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":56320,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalinformation.doc","url":"https://assets-eu.researchsquare.com/files/rs-7214810/v1/fc14f30d79fb017f4e35f158.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a multi-epitope vaccine against Mycobacterium tuberculosis using an immunoinformatics approach","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMycobacterium tuberculosis (MTB) is a significant airborne infectious agent in humans, causing tuberculosis (TB), highly contagious and has infected approximately one-third of the world's human population [1]. Tuberculosis (TB) is the leading global infectious disease killer [2], claiming 1.25\u0026nbsp;million lives just in 2023 [3], representing a substantial economic burden of disease across multiple countries [4], which poses an important threat to global public health [5]. According to the latest World Health Organization (WHO) report, among the estimated 10.8\u0026nbsp;million people who developed tuberculosis (TB) in 2023, males accounted for 55%, females 33%, and children 12% [3]. Since 2020, the incidence rate of Mycobacterium tuberculosis (TB) infection has risen by 4.6% [3]. Despite advances in TB diagnostic and therapeutic approaches, a substantial number of patients continue to be affected, making it the world's second deadliest infectious disease [6]. The Bacillus Calmette-Gu\u0026eacute;rin (BCG) vaccine, a live attenuated strain, has been the sole licensed tuberculosis vaccine since 1923 and is administered intradermally to prevent pulmonary TB [5]. Clinical evidence, however, reveals substantial heterogeneity in BCG's protective effectiveness against pulmonary TB across trial populations. Overall, global efficacy exhibits high variability due to factors including genetic divergence in BCG strains, host population heterogeneity, interference from non-tuberculous mycobacteria (NTM), and concurrent parasitic infections [7]. This inconsistent protection contributes to persistently high mortality and transmission rates [8,9]. While conventional BCG vaccination effectively prevents pediatric pulmonary TB, it demonstrates limited efficacy against adult pulmonary tuberculosis. Moreover, this live-attenuated vaccine poses significant virulence reversion risks when administered to immunocompromised individuals [10]. To date, only a limited number of countries incorporate BCG into routine immunization programs, while others have adopted targeted vaccination strategies following BCG-associated fatalities in immunocompromised children and documented adverse events [11]. Consequently, these multifaceted challenges significantly impede global tuberculosis (TB) control programs developing rapidly.\u003c/p\u003e\u003cp\u003eCurrently, multiple tuberculosis vaccines are in various stages of clinical trials; nevertheless, substantial safety concerns persist\u0026mdash;nearly 50% of candidate vaccines employ live attenuated Mycobacterium tuberculosis strains [12]. While viral-vectored vaccines (Crucell-Ad35, MVA85A) achieved only partial protection [13], recombinant candidates VPM1002/MTBVAC present virulence restoration concerns [14]. Subunit formulations like M72/H4 suffer from weak immunogenicity demanding repeated boosting [15]. The M72/AS01E vaccine, despite promising early results, induced localized adverse events at injection sites during Phase II evaluation [16]. Whereas, clinical trial data demonstrate considerable promise for multi-epitope subunit vaccines in combating tuberculosis [17,18]. Several research teams have employed immunoinformatics approaches to develop TB multi-epitope vaccines [19,20]. For instance, Jiang et al. recently designed the C624P multi-epitope vaccine against Mycobacterium tuberculosis through immunoinformatics analysis, which candidate exhibits exceptional antigenicity, immunogenicity, structural stability, and immune activation potential for latent TB infection (LTBI) prevention [21], reflecting growing momentum in TB peptide vaccine development.\u003c/p\u003e\u003cp\u003eThis study designed a computationally engineered multi-epitope peptide vaccine by integrating the most antigenic, non-allergenic, and non-toxic epitopes derived from four Mycobacterium tuberculosis (Mtb) antigenic proteins. Computational tools screened protein sequences to identify optimal B-cell, helper T-lymphocyte (HTL), and cytotoxic T-lymphocyte (CTL) epitopes. All candidate epitopes underwent rigorous antigenicity, allergenicity, and toxicity assessments prior to vaccine construction. The epitope linkage strategy was optimized to enhance structural flexibility and protein folding efficiency, addressing the global demand for more effective TB vaccines. Furthermore, the predicted immunological characteristics demonstrate vaccine immunogenicity and establish a theoretical foundation for subsequent experimental validation.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Retrieving Protein Sequences\u003c/h2\u003e\u003cp\u003eThe NCBI (National Center for Biotechnology Information) protein database [22] was utilized to search for target proteins. Subsequently, proteins were retrieved from Mycobacterium tuberculosis strain ATCC H37Rv: PPE family protein PPE18 ( NCBI ID: YP_177795.1), lipoprotein LpqG ( NCBI ID: NP_218140.1), ESAT-6 like protein EsxW (NCBI ID: NP_218137.1), and serine/threonine-protein kinase PknB (NCBI ID: NP_214528.1), obtaining the amino acid sequence of the adjuvant RpfE (Rv2450c). These data were acquired in FASTA format for integrated downstream analysis. The antigenic propensity of each selected protein was predicted using the antigenic peptide prediction tool [23]. Simultaneously, allergenic properties were identified via the descriptor-based fingerprinting server AllergenFP v.1.0[24]. To mitigate autoimmunity risks, all selected proteins underwent BLASTp screening against the human proteome with default parameters [25]. The multi-epitope vaccine design workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Epitope Prediction\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 B-Cell Epitope Prediction\u003c/h2\u003e\u003cp\u003eB-cell epitope prediction is crucial in vaccine design. Consequently, this study employed the ABCpred server for prediction [26]. ABCpred is an artificial neural network-based online server designed to identify linear B-cell epitopes within antigenic proteins. Amino acid sequences of selected proteins were submitted in single-letter code format without header lines, employing a threshold score of 0.51 with 16-mer epitope length, keeping on overlapping filter, and epitopes scoring\u0026thinsp;\u0026gt;\u0026thinsp;0.8 were selected as optimal candidates for downstream analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Helper T Lymphocyte (HTL) Epitope Prediction\u003c/h2\u003e\u003cp\u003eHTL epitopes of the chosen proteins were predicted using the IEDB MHC-II server [27]. The Immune Epitope Database and Analysis Resource (IEDB) is an online database and tool suite for predicting immune epitopes from experimentally validated collections [28]. Amino acid sequences of selected proteins were independently submitted in FASTA format. HTL epitope prediction was performed using default system parameters, with epitope length set to 15-mer, using the complete HLA human reference set for epitope prediction, and the resulting epitopes were ranked according to adjusted rank score with the lowest percentile rank were selected for further study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Cytotoxic T Lymphocyte (CTL) Epitope Prediction\u003c/h2\u003e\u003cp\u003eCTL epitopes were predicted using the IEDB MHC-I server [8]. Amino acid sequences of selected proteins were submitted in FASTA format. Employing the complete human HLA reference set, predictions used default IEDB recommended settings (2023.09 version, NetMHCpan EL 4.1) for 9-mer and 10-mer epitopes. Results were sorted by prediction score. Redundant HTL and CTL epitopes targeting identical HLA alleles were eliminated. Selected epitopes underwent further evaluation for antigenicity, allergenicity and toxicity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4 Epitope Antigenicity, Allergenicity, and Toxicity Prediction\u003c/h2\u003e\u003cp\u003eAll selected epitopes underwent evaluation for antigenicity, allergenicity, and toxicity. Antigenicity prediction used the VaxiJen v2.0 web server [29], and bacterial threshold: 0.4, which employs physicochemical properties to predict antigenicity through alignment-independent approaches. Allergenicity assessment utilized AllerTop v2.0 [30], implementing an auto-cross covariance (ACC) algorithm based on. Toxicity prediction used the ToxinPred server [31], with only antigenic, non-allergenic, and non-toxic epitopes selected for vaccine construction.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Vaccine Construction\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Subunit Vaccine Construction\u003c/h2\u003e\u003cp\u003eThe previously predicted epitopes and linker peptides were combined to construct a subunit vaccine. The B-cell, HTL, and CTL epitopes with the highest scores for antigenicity, non-allergenicity, and non-toxicity were linked via KK, GPGPG, and AAY, respectively. The amino acid sequence of the RpfE (Rv2450c) adjuvant was connected to the N-terminus of the vaccine construct via EAAAK. To screen for the optimal vaccine candidate, the antigenicity of the vaccine constructs was evaluated using VaxiJen and AntigenPro software, while their allergenicity was assessed using AllerTop V. 2.1.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Model Vaccine Homology Construction\u003c/h2\u003e\u003cp\u003eTo validate the structural stability of the selected protein, homology modeling of the vaccine construct was performed using web servers including I-TASSER [32], SWISS-MODEL [33], and PHYRE2 [34]. The generated prediction structural models were downloaded in PDB format to enable downstream analysis and validation procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Candidate Vaccine Structural validation\u003c/h2\u003e\u003cp\u003eTertiary structure prediction was performed via AlphaFold, enabling identification of conformationally optimal epitope placements [35], employing Chimera version 1.17.1 for 3D structural visualization [36]. Secondary structure elements (α-helices, β-strands, and coils) were subsequently profiled using PSIPREDv4.0 and GORIV web servers, which implement complementary neural network and information-theory frameworks [37,38].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Vaccine Construction Physicochemical characterization\u003c/h2\u003e\u003cp\u003eIn silico analysis of physicochemical characteristics was performed via Expasy's ProtParam web server tool, key parameters including protein length, isoelectric point (pI), molecular weight, half-life, instability index, aliphatic index, and grand average of hydropathy (GRAVY)[39] .\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e2.3.5 Vaccine Construction Molecular docking and dynamic simulation\u003c/h2\u003e\u003cp\u003eThe vaccine construct (antigen) was computationally docked against immune receptors TLR2 and TLR4 via the ClusPro 2.0 web server [40], which implements fast Fourier transform-based correlation approaches for binding pose prediction. Dominant binding conformations were rendered using UCSF Chimera version 1.17.1 for structural interrogation [41]. To evaluate the stability of the docked complexes under physiological conditions, molecular dynamics simulations (MDS) of the vaccine-TLR2 and vaccine-TLR4 complexes were performed using GROMACS 5.0 [42]. This analysis assessed structural integrity by monitoring four key parameters: root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Vaccine Population coverage analysis\u003c/h2\u003e\u003cp\u003eGiven the heterogeneity in human leukocyte antigen (HLA) allele frequencies across global populations, the protective efficacy of the designed vaccine was evaluated using the IEDB Population Coverage Calculation tool with default parameters to predict population-level immunoresponsiveness [43].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Codon optimization and cloning\u003c/h2\u003e\u003cp\u003eThe vaccine coding sequence was optimized for Escherichia coli expression using JCat, which maximizes translation efficiency through codon usage adaptation. Key optimization metrics, including GC content and Codon Adaptation Index (CAI) were recorded [44]. The synthetic gene was flanked by Xho1 and BamH1 NLS restriction sites at its termini and directionally cloned into the multiple cloning site of pET28a (+) vector, confirming construct fidelity via SnapGene tool.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Immunogenicity assessment\u003c/h2\u003e\u003cp\u003eThe immunogenicity response dynamics of our vaccine construct, in silico immune simulation was performed using the C-ImmSim server under default parameter settings [45], mimicking natural immune responses in humans. This tool agent-based immune simulator employs position-specific scoring matrices (PSSMs) coupled with machine learning algorithms to evaluate immunogenicity.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Protein sequence retrieval\u003c/h2\u003e\u003cp\u003eThis study retrieved four immunogenic proteins (ATCC H37Rv: PPE family protein PPE18 ( NCBI ID: YP_177795.1, length:391 aa ), lipoprotein LpqG ( NCBI ID: NP_218140.1, length:240 aa ), ESAT-6 like protein EsxW (NCBI ID: NP_218137.1, length:98 aa), and serine/threonine-protein kinase PknB (NCBI ID: NP_214528.1, length:626 aa). ) of the Mycobacterium tuberculosis H37Rv strain from the NCBI (National Center for Biotechnology Information) database and conducted immunogenicity prediction and physicochemical property analysis to construct a vaccine.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Epitope Prediction\u003c/h2\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 B-Cell Epitope Prediction\u003c/h2\u003e\u003cp\u003eEpitopes with scores greater than 0.8 predicted by the ABCpred web server were selected for further analysis of their antigenicity, allergenicity, and toxicity. Duplicate epitopes identified across different HLA groups were excluded from the study, and epitopes predicted to be non-antigenic, allergenic, or toxic by VaxiJen, AllerTop v.2.0, and ToxinPred, respectively, were also removed. Ultimately, 26 B-cell epitopes were identified for the four selected proteins (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrediction of B-cell epitopes for the input MTB protein sequences.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRank\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStart position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePPE18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGPGSASLVAAAQMWD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVRVAAAAYETAYGLTV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGSSLGSSGLGGGVAAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFGYAAATATATATLLP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eLPQG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVAPQYSNPEPAGTATI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRKDIRTTRVTVAPQYS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDALVGAGLDRKDIRTT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLSGCDSHNSGSLGADP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExsW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQNISGAGWSGMAEATS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLHGVRDGLVRDANNYE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"14\" rowspan=\"15\"\u003e\u003cp\u003ePknB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAGEVTGTNPPAGTTVP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSGPATKDIPDVAGQTV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGMLDKGADVDAGGSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e584\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLQKPDSTIPPDHVIGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGEPPFTGDSPVSVAYQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDVDAGGSQHNRVVYQN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePYIVMEYVDGVTLRDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eARDPSFYLRFRREAQN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLLSSAAGNLSGPRTDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eENRYQTAAEMRADLVR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGGMSEVHLARDLRLHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIGTAQYLSPEQARGDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEQARGDSVDARSDVYS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNGIIHRDVKPANIMIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTGEAETPAGPLPYIVM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Helper T Lymphocyte (HTL) Epitope Prediction\u003c/h2\u003e\u003cp\u003eBased on the corresponding four proteins, we selected seven HTL (helper T lymphocyte) epitopes with strong antigenicity, non-allergenic properties, and non-toxicity for vaccine construction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrediction of HTL epitopes for the input MTB protein sequences.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi.No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAllele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStart position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePPE18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DQA1*01:02/DQB1*06:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAELTAAQVRVAAAAY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DQA1*01:02/DQB1*06:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGQAELTAAQVRVAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DQA1*01:02/DQB1*06:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGQAELTAAQVRVAAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DQA1*01:02/DQB1*06:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQAELTAAQVRVAAAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPQG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB1*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRADQYAQLSGLRLGK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePknB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB4*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePDVAGQTVDVAQKNL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-DRB4*01:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIPDVAGQTVDVAQKN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9696\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Cytotoxic T Lymphocyte (CTL) Epitope Prediction\u003c/h2\u003e\u003cp\u003eAccording to the highest binding affinity to MHC I molecules, epitopes were screened and then ranked according to their percentile scores. Those in the lowest percentile rank were selected for antigenicity, allergenicity, and toxicity analyses. Duplicate CTL epitopes across different HLA combinations were excluded from the study, and candidate epitopes demonstrating antigenicity, non-allergenicity, and non-toxicity were chosen for further investigation. Ultimately, six CTL epitopes were identified for the PPE18, LPQG, EsxW, and PknB proteins, whereas no epitopes meeting the preliminary criteria were found for the PPE18 protein (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrediction of CTL epitopes for the input MTB protein sequences.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSi.No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProtein name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAllele\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSequence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStart position\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLPQG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*57:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVGFSVTVVW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.93074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExsW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*40:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQEQASQQIL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.971951\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*44:02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTEDEARRMW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.953543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003ePknB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*40:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eREAQNAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.988123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-B*40:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMEYVDGVTL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.986757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHLA-A*11:01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVIELQVSK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.97906\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Vaccine Construction\u003c/h2\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Subunit Vaccine Construction\u003c/h2\u003e\u003cp\u003eThrough stochastic rearrangement of selected epitope positions ordered sequentially as B-cell, HTL, and CTL epitopes, a randomized vaccine construct was generated. Concurrently, B-cell, HTL, and CTL epitopes were connected via KK, GPGPG, and AAY linkers, respectively. The RpfE adjuvant protein sequence was fused to the N-terminus of the vaccine construct using an EAAAK linker, and designed a 748 amino acid linear vaccine construct (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequent antigenicity and allergenicity predictions confirmed the construct's antigenic and non-allergenic properties.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Vaccine Homology and Structural Validation\u003c/h2\u003e\u003cp\u003eTo ensure the structural stability of the selected protein, we performed homology modeling of the vaccine construct using web servers such as I-TASSER, SWISS-MODEL, and PHYRE2. Subsequently, secondary structure elements were analyzed using PSIPRED v4.0 and GOR IV web servers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), revealing an Alpha helix (Hh) content of 42.65%, Extended strand (Ee) of 10.56%, and Random coil (Cc) of 46.79%, while all other metrics 3\u003csub\u003e10\u003c/sub\u003e helix (Gg), Pi helix (Ii), Beta bridge (Bb), Beta turn (Tt), Bend region (Ss), Ambiguous states, and Other states were 0.\u003c/p\u003e\u003cp\u003eTertiary structure prediction was performed using PHYRE2.0 to identify conformationally optimal epitope positions, and 3D structure visualization was conducted with SWISS-MODEL software (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The generated homology model exhibited optimal Ramachandran plot values. Structural refinement was subsequently carried out using GalaxyRefine (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Among the five models generated by GalaxyRefine, Model 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) demonstrated the most favorable metrics: a GDT-HA score of 0.971, RMSD value of 0.364, and MolProbity score of 1.837. After refinement, the percentage of residues in the most favored regions of the Ramachandran plot increased to 97.10%, showing significant improvement over the initial structure generated by SWISS-MODEL (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Based on these results, the optimized Model 5 was ultimately selected for molecular docking and dynamics simulation studies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Vaccine Construction Physicochemical characterization\u003c/h2\u003e\u003cp\u003eComputer-simulated analysis of the physicochemical properties of the vaccine was performed using the Expasy ProtParam web server tool. The protein length, isoelectric point (pI), molecular weight, half-life, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) were 748, 9.73, 77869.81, 4.4 hours, 25.74, 70.57, and \u0026minus;\u0026thinsp;0.475 respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e3.3.4 Vaccine Construction Molecular Docking and Dynamics Simulation\u003c/h2\u003e\u003cp\u003eMolecular dynamics simulation analysis reveals that the vaccine-receptor complex undergoes significant structural rearrangement during the initial 100 ps (RMSD increased from 0.10 nm to 0.20 nm, Rg decreased from 2.05 nm to 1.95 nm, SASA reduced from 155 nm\u0026sup2; to 150 nm\u0026sup2;) before reaching a stable state (RMSD stabilized at 0.15 nm\u0026thinsp;\u0026lt;\u0026thinsp;0.2 nm threshold, Rg\u0026thinsp;\u0026asymp;\u0026thinsp;1.98 nm\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 nm, SASA\u0026thinsp;\u0026asymp;\u0026thinsp;150 nm\u0026sup2; \u0026plusmn; 2 nm\u0026sup2;), confirming excellent structural stability. Simultaneously, RMSF analysis identifies high-flexibility sites in residue regions 20\u0026ndash;40, 80\u0026ndash;100, 140\u0026ndash;160, and 180\u0026ndash;200 (peaking at ~\u0026thinsp;0.25 nm), where these potential functional active zones and rigid structures (residues 50\u0026ndash;70, 110\u0026ndash;130) collectively maintain the complex's dynamic equilibrium (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), providing crucial molecular mechanism insights for optimizing candidate vaccine design.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Vaccine Population coverage analysis\u003c/h2\u003e\u003cp\u003ePopulation coverage analysis was conducted using the vaccine and its corresponding human leukocyte antigen (HLA) alleles to predict immune responses across diverse global populations. The study revealed that this vaccine achieves a global population average coverage of 88.22%. North America exhibited the highest coverage (100.0%), followed by Europe (99.92%) and West Africa (99.4%), while the lowest coverage was observed in South Africa (18.36%). Moreover, East Asia, Northeast Asia, Southeast Asia, Southwest Asia, East Africa, Central Africa, North Africa, West Indies, Central America, South America, Oceania were 89.24,96.11,99.0,81.05,79.39,96.48,95.56,77.17,89.3,92.54,99.39 and 98.61 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and information concerning regional divisions is presented in the supplementary materials. Therefore, the vaccine is expected to elicit efficacy across populations with diverse genetic backgrounds.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Codon optimization and cloning\u003c/h2\u003e\u003cp\u003eMaximizing vaccine expression levels in experimental research is particularly critical, where codon optimization of the amino acid sequence depends on the host expression system. This vaccine was codon-optimized for the E. coli expression system, resulting in a final sequence of 748 amino acids (2244bp). The Codon Adaptation Index (CAI) and GC content of the vaccine were 0.92 and 68%, respectively, both within the optimal range. Furthermore, the Xho1 and BamH1 restriction enzymes were selected and added to the N- and C-termini of the vaccine sequence, respectively. The cloning process of inserting the cDNA into the pET28a (+) vector was simulated in silico using the SnapGene tool (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Immunogenicity assessment\u003c/h2\u003e\u003cp\u003eThe immune simulation results generated by the C-ImmSim server indicated alignment with a potent immune response. As illustrated in the figures, our vaccine construct elicited robust primary and secondary immune responses. The primary immune response was characterized by elevated IgM antibody levels following a 15\u0026ndash;20 day latent period after antigen exposure. The secondary immune response exhibited accelerated B-cell proliferation and increased expression of IgM, IgG1\u0026thinsp;+\u0026thinsp;IgG2, and IgG\u0026thinsp;+\u0026thinsp;IgM antibodies. This vaccine construct not only effectively stimulated B-cell proliferation but also induced the generation of memory B cells. It simultaneously triggered a strong helper T-cell and cytotoxic T-cell immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMycobacterium tuberculosis (MTB) is a significant contributor to global mortality, posing a grave threat to global human health. Although multiple anti-tuberculosis drugs are commercially available, their therapeutic efficacy remains limited, while the pathogen has developed substantial drug resistance, rendering its multidrug-resistant properties well-documented. Consequently, preventing drug-resistant tuberculosis stands as a critical priority for global health efforts [46]. Bacillus Calmette\u0026ndash;Gu\u0026eacute;rin (BCG) remains the sole available tuberculosis preventive vaccine [47]. While it demonstrates proven efficacy against childhood extrapulmonary TB, its protective effect against adult tuberculosis is minimal to nonexistent. Variations in BCG efficacy are attributable to genetic divergence among BCG strains resulting from differential culturing protocols across laboratories [48]. Concurrently, prior exposure to environmental mycobacteria and the induction of short-lived T effector memory cells contribute to BCG's failure to fully prevent mycobacterial infection, leading to BCG's genomic deletions in RD (Region of Difference) regions and absence of several key protective antigens [49].\u003c/p\u003e\u003cp\u003eIt is well established that vaccine development constitutes an extensive and complex process, demanding not only substantial financial investment and time commitment, but also rigorous laboratory validation of safety and efficacy. However, with advances in new-generation information technologies and interdisciplinary convergence, immunoinformatics has pioneered novel methodologies for pharmaceutical and vaccine research, propelling the field into an accelerated development trajectory. Immunoinformatic approaches have been repeatedly employed to develop vaccines against diverse pathogens, demonstrating compelling efficacy outcomes. Several candidates are now in late-stage clinical development, including those targeting salmonella enterica, streptococcus pyogenes, brucella spp, staphylococcus aureus, and dengue virus [50\u0026ndash;54]. However, prior studies by Shiraz et al. and Sharma et al.primarily focused on epitope-based vaccine design through antigen selection without conducting population coverage analysis [8,55]. In contrast, our study incorporates comprehensive population coverage assessment, yielding critical insights into vaccine applicability across human populations.\u003c/p\u003e\u003cp\u003eTo address the urgent global need for effective tuberculosis interventions, we designed a multiepitope vaccine incorporating four immunogenic antigens. Immunogenicity, homology, allergenicity, and toxicity of each antigen were assessed through independent predictions of B-cell (BCL), helper T-cell (HTL), and cytotoxic T-lymphocyte (CTL) epitopes. Selected epitopes were subsequently assembled via linker peptides into a candidate vaccine construct, ensuring proper protein folding and structural flexibility. The construct integrates adjuvant RpfE (Rv2450c) conjugated to epitopes using EAAAK linkers to form a three-dimensional architecture [56]. Epitope-specific linkers were strategically implemented: GPGPG spacers induced HTL responses while minimizing junctional immunogenicity [57]; AAY (Ala-Ala-Tyr) linkers enhanced vaccine immunogenicity by reducing neo-epitope formation [58]; KK dipeptides optimized overall immunogenic potential [59]. To potentiate immune activation, RpfE adjuvant was tethered to the N-terminal epitopes via EAAAK linkers. The resulting construct demonstrated favorable biosafety profiles (non-allergenic, non-toxic), strong antigenicity, and 88.22% global population coverage. Further codon optimization and in silico cloning yielded ideal codon adaptation index (CAI\u0026thinsp;=\u0026thinsp;0.92) and GC content (60%). Finally, tertiary structure modeling with validation through Ramachandran plot analysis (91.7% favored regions), and RMSD (0.364) confirmed conformational stability, indicating this vaccine elicits robust immune responses against Mycobacterium tuberculosis (MTB), establishing a novel strategic framework for global tuberculosis prevention and control. Therefore, its implementation will offer significant safeguards for advancing global health equity and maximizing population health rights and interests.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study selected four Mycobacterium tuberculosis (MTB) antigenic proteins to construct a multiepitope TB vaccine. Using diverse immunoinformatic approaches, we identified immunodominant and non-allergenic peptides from these antigens. Preliminary assessment of the candidate vaccine's structural, immunological, and physicochemical characteristics suggests its potential to elicit cell-mediated immune responses against MTB, positioning it as a lead vaccine candidate. Subsequent in vitro and in vivo evaluations will be conducted, utilizing animal models to validate the vaccine's expression profile and immunogenic potency to further determine its safety and protective efficacy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWF and SZY conceived the study. FLJ，WXP contributed to study design/supervision. WF and CMB conducted data integration/analysis. The manuscript was drafted by WF and SZY, revised by all authors, and finalized with critical input from WF. All authors reviewed and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ningxia Natural Science Foundation Project (NO: 2023AAC03516); State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (NO: SKL-HIDCA-2024-35).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or examined throughout this research has been included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study contains no studies with human participants and animals performed by authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePalittapongarnpim P, Tantivitayakul P, Aiewsakun P, Mahasirimongkol S, Jaemsai B. 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Vaccine. 2005;23(17–18):2322–2326.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7214810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7214810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMycobacterium tuberculosis (Mtb), a highly lethal pathogen, faces exacerbated threats from drug-resistant strains. Given the limited efficacy of the licensed BCG vaccine against adult pulmonary TB and latent infection reactivation, novel vaccines are imperative. This study designed an immunoinformatics-driven multi-epitope vaccine by screening target proteins for B-cell, cytotoxic T-lymphocyte (CTL), and helper T-lymphocyte (HTL) epitopes, fused with RpfE adjuvant. The construct demonstrated antigenicity, solubility, and non-allergenicity in physicochemical analyses. Molecular docking, dynamics simulations, and immune modeling predicted robust immune responses, while in silico cloning confirmed feasibility. Though promising as a TB vaccine candidate, in vivo validation of protective efficacy remains essential.\u003c/p\u003e","manuscriptTitle":"Development of a multi-epitope vaccine against Mycobacterium tuberculosis using an immunoinformatics approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 18:18:01","doi":"10.21203/rs.3.rs-7214810/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ddce762-86dc-40c0-b60f-bd9bb713a1bf","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53141971,"name":"Biological sciences/Biotechnology"},{"id":53141972,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53141973,"name":"Biological sciences/Drug discovery"},{"id":53141974,"name":"Biological sciences/Immunology"},{"id":53141975,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-04T07:56:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 18:18:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7214810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7214810","identity":"rs-7214810","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}