Design and validation of multi-stage expressing mRNA vaccine for Mycobacterium tuberculosis through computational technology

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Despite several scientific interventions being carried out for its diagnosis and treatment, it is still the leading cause of mortality from any known infectious disease. Novel approaches for a promising TB vaccine necessitate identification of antigens that could confer protective immunity at all stages of infection and boosting immune response. mRNA vaccines are the vaccines of the future and offer a viable substitute for traditional vaccine techniques. Employing an immuno-informatics approach, we designed mRNA vaccine with T cell- epitopes expressed through different stages of TB infection. In-silico results using IEDB and NETMHC4.0 shows strong affinity of designed vaccine for both class I MHC restricted CD8 T cells and class II MHC restricted CD4 T cells. Our designed mRNA vaccine based on selected epitopes along with extra co-translational mRNA structural component was predicted to be highly stable in RNA fold webserver. Codon optimization led to the optimal translation of the mRNA in the host cell. Molecular docking in Patchdock revealed strong interaction of designed construct for immune receptors- TLR2 and TLR4 which is further confirmed by MD simulations using Gromacs server. Both these receptors establish specific Leucine-Rich-Repeats (LRR) regions interactions with the vaccine construct, implicating their strong binding affinity for these immune receptors. C-Immsim based immune simulation studies substantiated translated protein immunogenic nature as a promising vaccine. Our approach of immunoinformatic based designing, synthesis and experimental validation of mRNA vaccine could be a promising strategy to combat TB. Mycobacterium tuberculosis immuno-informatics mRNA vaccine peptide-based vaccine candidates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Millions of people worldwide are affected by Tuberculosis (TB), which is caused by the bacterium Mycobacterium tuberculosis (Mtb). According to WHO report 2024 approximately 10.8 million individuals were infected and 1.9 million died from this infectious disease (WHO, 2024). Treatment is time-consuming and inefficient due to the emergence of drug-resistant strains (Nguyen, 2016) (Glynn et al., 2002) . A potential post exposure vaccine is a promising approach for reducing antibiotic resistance and impeding the global spread of disease(Hasso-Agopsowicz et al., 2024) (Garbuglia et al., 2020) (Frost et al., 2023). Currently, Bacillus Calmette-Gurein (BCG) is the only licensed prophylactic vaccine which prevents TB infection in paediatric patients but is ineffective in adults. During the COVID-19 pandemic, mRNA vaccine was given emergency authorization and are now being synthesised for other infectious disease. For many viral infections like HIV, dengue, Zika, influenza; mRNA vaccines are undergoing different stages of clinical trials (Jain et al., 2020). Some mRNA vaccines for TB have also undergone immunological confirmation at in silico stage and only one in vivo stage (Al Tbeishat, 2022; Larsen et al., 2023; Shahrear & Islam, 2023; Zhu et al., 2024). The continuous efforts of more than two decades have resulted in 12 TB vaccine candidates under active clinical trials as of September 2024 with only two mRNA vaccines BNT164a and BNT164b (WHO, 2024). All these TB vaccines under trial constitutes of early stage expressing whole proteins, viral vector or live attenuated/killed whole Mtb based formulations. On the other hand, several subunit vaccines are under advanced clinical trials, but these are having low immunogenic activity(Schrager et al., 2020). The low immunogenicity of protein subunit vaccines and use of extensive cell culture and intricate purification schemes makes them less attractive that are frequently protein specific. The mRNA vaccines, on the other hand, are quick to produce, scalable and cost-effective because they can be synthesized in a single cell-free reaction and have a standard purification process, regardless of the antigen sequence (Kis et al., 2020) (Park et al., 2021). Moreover, subunit vaccines necessitate use of adjuvants such as AS01, CAF01, GLA-SE, and IC31(Agger, 2016). These adjuvants have some inflammatory and reactogenic issues that need to be resolved (Stewart et al., 2019) . mRNA vaccine can generate better immune response even in the absence of adjuvant. Few studies compared the adjuvanted subunit vaccine and mRNA vaccine and found that mRNA vaccine induce both humoral and cellular immune response against the pathogen (Lin et al., 2023) (Monslow et al., 2020) (Wu et al., 2022). The use of mRNA vaccine has other beneficial features such as low infection risk, high stability, translational ability, amplifiable in-vivo half-life, efficient delivery and expression in the host (Kariko et al., 2008) (Kauffman et al., 2016) (Gaun et al., 2017) (Thess et al., 2015). Thus, it directed us to design an mRNA vaccine for TB instead of a peptide-based vaccine. One important aspect we considered while designing vaccine is the multistage nature of Mtb infection where it can either actively replicate causing disease symptoms or remain dormant causing latent infection(Bellini et al., 2023; Yang et al., 2024). Therefore, the selection of antigens was crucially done which could generate protective response against different stages of TB. Among the early stage expressing antigens of Mtb, three proteins were selected MPT64 (Rv1980c), ESAT-6 (Rv3875), and CFP10 (Rv3874). From the late-expressing proteins, two proteins were from dormancy associated regulon DosR family, (Rv1738 and Rv1733c) and one from the Glycosyl hydrolases family 65 (Rv2006). The constitutively expressed protein from PE-PGRS family included was WAG22 (Rv1759c). Among 16 Mammalian Cell Entry (Mce) operon proteins, Mce4A and Mce4F proteins, which are specifically known for host cell invasion were also incorporated in the vaccine (Saini et al., 2008) (F. Zhang & Xie, 2011)(Rodríguez et al., 2015). We expect that this vaccine construct can fight Mtb at different stages of infection. During infection, the interaction of bacterial pathogen associated molecular patterns (PAMPs) with the host pattern recognition receptors (PRRs) is the prime step that activate various immune cells (Mogensen, 2009). Among the various classes of PRRs, Toll-like receptors (TLRs) are key players in elicitation of the immune response against Mtb specifically TLR2 and TLR4 (Heldwein & Fenton, 2002)(Pattanaik et al., 2022). Activated immune cells further process and present the Mtb derived antigenic peptides and mount them on Major Histocompatibility Complex (MHC) molecules for priming of T lymphocytes. CD4+ helper T cells are primed by MHC class II peptide epitopes while CD8+ cytotoxic T cells are primed by MHC class I peptide epitopes (Furuta et al., 2012) (Li et al., 2013). Primed T cells lead to release of repertoire of pro-inflammatory cytokines which are implicated in macrophage activation (Bettencourt et al., 2020; Chai et al., 2020; Khan et al., 2021). In this study, we have employed an immuno-informatics approach for designing a novel mRNA vaccine for the multi-stage TB infection. The translated product of mRNA vaccine has eighteen promiscuous antigenic epitopes as described above which are strong binders of class I and class II MHC alleles. Also, these epitopes are pro-inflammatory, non-toxic, non-allergenic with wide population coverage. The translated product of mRNA vaccine was also predicted to have strong binding affinity for TLR2 and TLR4 receptors. Furthermore, simulation studies predicted the elicitation of strong Th1 type protective immune response for our vaccine construct with memory T cells lasting for months and secretion of pro-inflammatory cytokines. 2. Materials and Methods 2.1 Schematic work plan for designing mRNA vaccine- A snapshot of in-silico work plan for designing of mRNA vaccine is depicted in Figure1. 2.2 Selection of promiscuous antigenic epitopes for mRNA vaccine construct The retrieved protein sequences were submitted to NetMHC4.0 server for predicting MHC I restricted CD8 T-cell epitopes and in IEDB server for MHC class II restricted CD4 T-cell epitopes (Lundegaard et al., 2008)(Nielsen et al., 2007). NetMHC4.0 server adopts Artificial Neural Network (ANN) algorithm with 81 different Human Leucocytes Antigens (HLA) alleles. Peptide length of 9-mer was selected with recommended threshold value of 0.5% for strong binders and 2% for weak binders. IEDB server is based on Stabilization Matrix Method (SMM) align and evaluates the binding affinity of 15mer peptides for the 15 documented HLA-DR alleles based on the Inhibitory Concentration (IC50) value. Lower IC50 value is proportional to high binding affinity between peptide-MHC complex (Fleri et al., 2017). Strong binders were selected with default IC50≤250 score. Characterization of promiscuous epitopes was done based on their binding affinity to three or more alleles of class I, class II or both. All the promiscuous epitopes were analysed for their antigenicity through Vaxijen2.0 sever where a score of >0.4 was considered antigenic. 2.3 Elimination of cross-reactive peptide The promiscuous antigenic epitopes were analysed against the annotated human proteins. NCBI protein blast (https://blast.ncbi.nlm.nih.gov/Blast.cgi) search was done for the selected peptide epitopes against human genome. The peptides which were found to be 100% identical with the human genome were excluded from further analysis. 2.4 Evaluation of pro-inflammatory, non-allergenic and non-toxic nature of selected epitopes Pro-inflammatory peptides that can induce IFN-γ, TNF-α, IL-12, IL-18 and IL-8 cytokines were selected using PIP-EL server. AIPpred server was used for evaluating the anti-inflammatory nature of selected promiscuous, antigenic peptide epitopes(Manavalan et al., 2018a). Epitopes which were having more PIP-EL score than AIPpred score obtained were considered pro-inflammatory. The allergenicity and toxicity of the promiscuous antigenic pro-inflammatory peptides were determined by AllerTOPv2.0 and Toxin-Pred server. AllerTOPv2.0 predicts the allergenicity of sequence based on physicochemical properties (Dimitrov et al., 2014). The promiscuous antigenic epitopes that were non allergen were then submitted to Toxin-Pred server to analyse the toxicity of the peptides. The peptides with score 0.0 were selected and the peptides with more than 0.0 were considered toxic and excluded from the study (Gupta et al., 2013). 2.5 Population coverage of selected epitopes There is wide range of diversity in MHC alleles depending upon population geography and ethnicity. Analysis of population coverage was done by IEDB population coverage tool with the selected epitopes binding to both MHC class I and MHC class II alleles (http://tools.iedb.org/population/). 2.6 Designing of mRNA vaccine construct The shortlisted epitopes for vaccine construct were promiscuous, antigenic, non-allergenic, non-toxic and pro-inflammatory. The vaccine construct was designed by incorporating these epitopes using the linkers. Since our aim was to design mRNA vaccine therefore to make a stable mRNA vaccine following modifications were incorporated in the mRNA sequence. Addition of 7-methyl guanosine sequence was done at 5' end. Subsequently RNA sequence of β-globlin was attached after 7-methyl guanosine as 5' Untranslated region (UTR). Kozak Sequence was also incorporated after 5' UTR region to enhance the translation initiation efficiency. Followed by Kozak sequence, Tissue plasminogen activator (tPA) signal sequence was attached to enhance immunogenicity (Kou et al., 2017a) (Uniprot ID-P00750). Signal sequence of MHC-I targeting domain (MITD) at 3' end to target the CTL epitopes to MHC I region of endoplasmic reticulum (Kreiter et al., 2008) (Uniprot ID-Q8WV92). Sequence of α-globlin was added after MITD sequence as 3' UTR. Poly (A) tail that works synergistically with 5' cap to stabilize the mRNA was also linked at 3' end. 2.7 Stability and secondary structure prediction of designed mRNA vaccine The stability of the mRNA vaccine was estimated using RNAfold webserver (Gruber et al., 2015). This server also predicts the secondary structure of mRNA. Loop based energy model and dynamic programming algorithm was employed for determination of secondary structure. This server predicts the thermodynamic stability of mRNA and generates a minimal free energy score (MFE). Furthermore, mountain plot was also obtained. This plot is combinatorial graph of MFE, centroid structure and pair probabilities. The centroid mRNA structure gives the minimal base pair distance to all other secondary structure in the Boltzmann ensemble. MFE based secondary structure defines functional motifs of mRNAs which further determine the thermodynamic properties. All these parameters of our mRNA vaccine construct were compared with wild retinoblastoma (Rb1) mRNA taken as a positive control. 2.8 Codon optimisation and In - silico cloning of mRNA vaccine construct The designed construct was further subjected to Java codon adaptation tool (JCAT) server for codon optimization in E.coli strain K12 (Grote et al., 2005). The sequence predicted by JCAT server was employed for in silico cloning using Snap Gene server in pET28:GFP vector. 2.9 Homology modelling and structural validation of vaccine construct The mRNA vaccine construct was translated into protein and the translated protein was modelled in I-TASSER server (Roy et al., 2010). The C-score depicts the accuracy of model; considering the value between -5 and 2, the higher C value signifies the higher stability of the model. Five models in ITASSER were generated and subjected to Chiron server for energy minimization. Best structure was selected through structural validation in PROCHECK, ERRAT and VERIFY 3D web server available in UCLA SAVES. In PROCHECK, the stereo-chemical nature of structure is predicted via Ramachandran plot (Laskowski et al., 1996). ERRAT and VERIFY 3D evaluates summative quality factor and the best comparative structure of the model respectively (Colovos & Yeates, 1993). 2.10 Physicochemical properties of translated vaccine construct ExPaSy Database server ProtParam was used to analyse the physicochemical properties of the translated vaccine construct which predicted sequence length, molecular weight, stability index, grand average of hydropathicity (GRAVY) and half-life. Furthermore, the allergenicity, toxicity and pro-inflammatory nature of the whole construct were also predicted as described previously (Dimitrov et al., 2014) (Manavalan et al., 2018b) (Gupta et al., 2013). 2.11 Secondary Structure of construct Details of secondary structure of translated vaccine construct was predicted by the GOR (Garnier-Osguthorpe-Robson) IV sever (Sen et al., 2005). GOR server is based on information method where information’s generated by each residue were combined to predict function of a protein. 2.12 Docking of translated vaccine construct with TLRs immune receptors The binding affinity of the translated vaccine construct with TLR2 and TLR4 was estimated through PATCHDOCK and FIREDOCK severs (Schneidman-Duhovny et al., 2005)(Mashiach et al., 2008). Binding energy score between vaccine construct and receptor was generated by Hex8.0 server and the interactions were visualized by Discovery Studio 4.0. The various LRR regions in the receptor residues binding with specific amino acid residue of vaccine construct was also visualized by Discovery Studio 4.0 (https://discover.3ds.com/discovery-studio-visualizer-download). 2.14 Molecular dynamic simulation The docked complex of the translated mRNA construct and TLR2 was then subjected to MD simulation using Gromacs software 2020.5(Refianti et al., 2011). Using CHARMM36 force field, the docked complex was solvated by TIP3P (transferable intermolecular potential with 3 points) water model(Kern et al., 2024). Structures generated were then run for MD simulation at 300K temperature and standard pressure of 1.01 bar using NPT (number of particles [N], system pressure [P] and temperature [T]) and NVT (number of particles [N], system volume [V] and temperature [T]) ensemble. The trajectory was then analysed by Root Mean Square Fluctuation (RMSF) and Mean Square Deviation (RMSD). 2.13 In-silico Immune simulation studies of the translated vaccine construct C-ImmSim server predicts the immune response evoked by any protein (Rapin et al., 2010). The protocol in C-Immsim was defined with two injections of 50 µl each within intervals of 4 weeks. First dose was set as one and the booster dose at 84-time steps. The time step selected to be 1000 and 12,345. LPS was precluded from the injection. Although we identified only T-cell epitopes to make mRNA vaccine, we also searched for B-cell epitopes. ABCpred software with threshold of 0.51 was utilised to find the same. 3. Results 3.1 Selection of promiscuous antigenic and proinflammatory epitopes for mRNA vaccine construct All nine selected proteins were passed through MHC class I and MHC class II epitope prediction software where 714 and 134 promiscuous antigenic epitopes were obtained respectively. Out of 87 overlapping promiscuous antigenic epitopes which bind to both MHC class I and MHC class II alleles, 18 epitopes were selected after removing self-peptide. There antigens were further evaluated for being pro-inflammatory, antigenic, nontoxic and non-allergenic properties (Table S1). 3.2 Evaluation of pro-inflammatory, anti-allergenic and non-toxic nature of selected epitopes Among 18 selected promiscuous antigenic epitopes, all were found to be pro-inflammatory in nature. TSVRVAAAL epitope have highest pro-inflammatory score 0.6328 whereas LVVFPALFA have lowest pro-inflammatory score 0.4998 among the selected peptides. All the promiscuous antigenic epitopes were found to be anti-allergenic and non- toxic in nature. These epitopes were used for construction of mRNA vaccine. Table S2 enlists the characteristic scores obtained for selected epitopes along with binding profile to both MHC alleles. 3.3 Population coverage of shortlisted epitopes T cell recognized epitope in a population with a specific HLA composition may not elicit response in another population with different HLA composition. Therefore, it becomes essential to select epitopes that bind to numerous HLA supertypes thereby ensuring broad coverage across different populations. Selected epitopes corresponding to their respective alleles have been studied for their population coverage percentage in the different countries. Figure 2 depicts the specific coverage region of the selected epitopes. The global coverage stands for 99.98% which entails quite high allelic distribution across the world. Almost all the regions showed population coverage in the range of 97-99.9% . 3.4 Designing of mRNA vaccine construct The mRNA vaccine construct consisted of following elements sequenced from N terminal to C terminal: 5'm 7 G cap - 5'UTR (β globin) – Kozak sequence – tPA (Signal peptide) – Mce peptide epitopes – KPKPKP linker – Early, Late and Consistent expressing protein epitopes linked via AAY linker – MITD signal sequence - 3'UTR (α globin) – Poly (A) tail (Figure 3). 3.5 Evaluating the stability of mRNA vaccine construct The stability of secondary structures of our designed mRNA construct was evaluated by RNAfold web server and it predicted that our secondary structure has optimal free energy of -1688.60 kcal/mol which is more than wild Rb1 mRNA having -1154kcal/mol free energy (Uddin et al., 2015) . Interestingly, our designed mRNA construct consists of -1141.82 kcal/mol of centroid free energy which is more than free energy (-827.99kcal/mol) generated by Rb1 mRNA. Thermodynamic ensemble energy of our designed mRNA vaccine was also comparable to the wild Rb1 mRNA. The mountain plot also confirms the stability of the designed mRNA and predicts that it remains stable after manufacturing (Figure 4). 3.6 Codon optimisation and in-silico cloning of mRNA vaccine construct The optimised codon sequence has 696 nucleotides. The codon adaptation index (CAI) value was predicted 1.0, and the average GC content of the adapted sequence was 55.2%, which indicates high expression in the E. coli host. Finally the adapted codon sequence was in silico cloned in pET28:GFP vector (Figure 5) using snap-gene server. 3.7 Structural validation of the translated vaccine using I-TASSER I–TASSER generated 5 tertiary structures of the construct; based on all analyzed parameters model 1 (Figure 5 (A)) with C-score of -1.85 was selected. The overall quality factor was 86.6% in ERRAT and VERIFY 3-D score was 39.66%. Ramachandran plot shows 71.6% residues residing in favored region, 20.5% residues in generously allowed regions and 3.3% residues in disallowed regions (Figure 6). 3.8 Physiochemical Properties of translated vaccine construct The translated vaccine construct is 232 amino acid sequence with molecular weight of 24.59kDa. The theoretical PI score of 9.38, indicating the construct to be alkaline in nature. Half- life of the designed construct is >100 hours (mammalian reticulocytes, in vitro ), > 20hrs in yeast ( in vivo ) and >10 hrs. in E.coli ( in vivo ) implicating the structure is stable in vivo. The instability index was computed to be 30.56 classifying our protein to be stable. The Grand Average of Hydropathy (GRAVY) and aliphatic score were estimated to be 0.604 and 30.56 respectively. The translated construct was predicted to be non- allergenic, antigenic with a score of 0.6465 and pro-inflammatory in nature with PIP score 0.6624. 3.9 Predicting secondary structure of the vaccine construct The secondary structure of the Construct in GOR4server predicts 81.8% of residues in the alpha helix region, 16.33% forms random coil structure and 1.72% of residues forms extended strand structure (Figure 7). 3.10 Translated vaccine construct show strong interactions with TLRs Interaction of ligands with TLRs activate signaling cascade that are essential for the efficacy of vaccine. So, the structurally validated designed vaccine model was docked with TLR2 and TLR4 to evaluate their affinity towards the immune receptors. Our translated version also showed strong affinity for TLR2 as well as TLR4 receptors. Vaccine construct-TLR2 docked complex showed 13 hydrogen bond interactions and high energy score of -859.56kcal/mol (Figure 7a). LprG, a known TLR2-agonist was included as a positive control and showed a comparative docked energy score of -832.93kcal/mol with TLR2. Interestingly, our vaccine construct facilitated strong interactions involving the LRR regions of TLR2. There were 20 bonds (11 H-bonds, 2 electrostatic bonds and 7 hydrophobic bonds) out of the total 26 interactions that were mediated with various LRR regions of TLR2 (S3). Vaccine construct-TLR4 docked complex showed even better binding affinity evident by high Hex energy score of -933.50kcal/mol. The rpfB protein of Mtb which is reported to be TLR4-agonist showed an energy score of -718.95kcal/mol with TLR4 (Figure 8b). Additionally, there were 9 LRR regions of TLR4 involved in bonding interactions with our vaccine construct. 3.11 MD simulation of docked complexes MD simulation was performed with the stable docked structure of the translated mRNA vaccine and TLR2 for 100ns. RMSD analysis of the trajectory shows that the complex backbone initial increased for 5ns and then reaches a stable state with an average value of 0.4nm to 0.6nm for 100ns (Figure 9a). The average RMSF value of docked complex was 0.30nm (Figure9b). These findings suggest that complex structures have higher rigidity and are more stable with a low variation. 3.11 Evaluation of immune response generated through C-ImmSim server The mammalian immune response was evaluated by C-ImmSim server. There is a significant increase of pro-inflammatory IFN-γ, IL-12, IL-2 with the booster administration of our designed vaccine (Figure 10). The T- cells were shown to generate quite a high number of memory cells persisting for several months. Although, designed as T-cell specific peptide-based vaccine, it is also predicted to evoke humoral immune response evident by secretion profile of IgG1 class antibodies. Also, the designed vaccine was able to generate antigenic B-cell epitopes in ABCpred software. Thus, exposure of our vaccine can generate both humoral and cell-mediated immune response. Discussion In-silico methods have created momentum for vaccine designing in the scientific community and have accelerated the possibility to attain the goal of ‘End TB Strategy’ by 2030. Most of the antigens selected as vaccine candidates and under pre-clinical trials are early expressing antigens of Mtb. Considering the infection spectrum of Mtb, we have selected a combination of nine antigenic proteins. Among these, early expressing proteins (ESAT-6, CFP-10 and MPT-64), constitutively expressing protein (Wag22), proteins expressing at dormant phase (Rv2006, Rv1738 and Rv1733c) and proteins helping the entry of pathogen into the host (Mce4A and Mce4F), were found to be highly immunogenic. (Brandt et al., 2000) (Passos et al., 2024)(Valizadeh et al., 2022) (Campuzano et al., 2007) (Murphy et al., 2005) (Kassa et al., 2012) (Zvi et al., 2008)(Saelee et al., 2022) (W. Zhang et al., 2014) (Hemati et al., 2019). All these nine antigenic proteins were used to generate 18 promiscuous antigenic epitopes having strong binding affinity for both MHC class I and class II alleles. It is required for generation of effective cell-mediated immune response which play protective role against TB pathogenesis and bacterial clearance (Jasenosky et al., 2015). Incorporation of AAY and KPKPKP linkers in our mRNA vaccine candidate is predicted to enhance immunogenicity (Wang et al., 2004) (Jafari et al., 2020). The translational efficiency and half-life of mRNA vaccine candidate is increased by addition of poly(A) tail, human β-globin gene at 5′ UTR and human α-globin gene at 3′ UTR (Passmore & Coller, 2022) (Ma et al., 2024). Human β-globin gene help in stabilizing ribosomal binding at 5’UTR and α-globin gene help in effective termination of translation at 3’UTR (Ryczek et al., 2023). MITD sequence incorporated in our vaccine construct is known to stimulate the antigen-specific immune response. This stimulation is because of MITDs ability to mimic the natural transport characteristics of MHC I molecules. Due to the cross presentation, MITD sequence was found to stimulate both CD4+ and CD8+ cells (Y. Zhang, Zhai, Huang, et al., 2024)(Zhang, Zhai, Qin, et al., 2024). The tPA signal sequence targets mRNA to endoplasmic reticulum and improves immunogenicity (Zhang, Zhai, Qin, et al., 2024)(Kou et al., 2017b). The free energy and centroid score of our mRNA vaccine was higher as compared to human Rb1 mRNA that indicates the mRNA vaccine is stable in human body. Our designed mRNA vaccine is translated, the stability and immunological profile was evaluated for effective immune response. The computational analysis of the translated mRNA vaccine predicted that the vaccine is highly antigenic, pro-inflammatory, non-toxic, and thermostable with longer half-life in the human reticulocyte, making it as a possible mRNA vaccine candidate. The codon optimisation of the mRNA vaccine showed 55.2% GC content and CAI value 1 indicates its efficient expression in the host(Jin et al., 2025). Our vaccine’s secondary structure analysis revealed that 81.8% of helical regions can add rigidity, compactness and facilitate better interaction with host immune receptors (Bromley & Channon, 2011). More the negative energy score between vaccine and receptors (TLR2 and TLR4) suggests the high binding affinity. LRRs specific interaction are important for activation of TLRs signalling and generation of innate immune response(Behzadi et al., 2021). Through molecular docking analysis translated vaccine construct was found to interact with TLR2 and TLR4 within LRRs specific regions. The interaction of translated mRNA vaccine with TLR4 was verified using MD simulation. The i n-silico immune simulation predicted robust enhancement of immune response generated by dual dose administration of vaccine. Our vaccine construct was found to generate cell mediated immune response against tuberculosis which is crucial in controlling the infection and observed high tighter of antibody generation. The cationic peptide incorporated in the vaccine may be helpful in generation of humoral immune response along with cellular immune response (Fritz et al., 2004). The combinatorial increase of B and T- cells activity lasting for long time, along with the production of pro-inflammatory cytokines and activated macrophages substantiates it to be a promising vaccine candidate. Although mRNA vaccine has several advantages, but they also have limitations including their instability on storage and delivery. To overcome this mRNA vaccine can be delivered by encapsulating with lipid nano particle or dendritic cell (DC) membrane coated nanoparticles for targeted delivery(Hou et al., 2021) (Cao et al., 2023). This delivery system can be administered intramuscularly leading to potential adaptive immune system activation by recruiting various DC subsets which can prime T and B cell(Y. Zhang, Zhai, Qin, et al., 2024)(Liang et al., 2017). In this study, we identified dominant T cells epitopes through a series of computational analyses and converted them into multi-epitope mRNA vaccine. The designed vaccine exhibits favourable immunodominant characteristics and solubility. Using molecular docking, and in silico cloning, it was found that the mRNA vaccine-TLR2and TLR4 have stable interaction. Once transported in vivo it can be expressed into protein. This protein is predicted to be stimulating prolonged cellular and humoral immunity with broad population coverage. Based on our findings, we believe that the vaccine candidate could be a starting point for the development of effective vaccines targeting different stages of Mtb infection. Furthermore, it is essential to conduct future experimental studies initially on PBMCs of patient and then on pre-clinical murine models to confirm the immunogenicity of this mRNA vaccine. Declarations CRediT authorship contribution statement Prof. Sadhna Sharma: Conceptualization, Methodology, Software, Data curation, Visualization, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition. Dr. Monika Sharma: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Funding acquisition. Manika Sharma: Methodology, Software, Data curation, Visualization, Validation, Formal analysis, Writing – original draft, Writing – review & editing. Parul Bhatt: Methodology, Software, Visualization, Validation, Formal analysis, Writing – review & editing, Supervision. Medha - Methodology, Software, Data curation, Visualization, Formal analysis, Writing – original draft. Declaration of Interest- We declare that the authors have no competing interests. Acknowledgement- The funding by Department of Science and Technology (DST) SERB POWER grant SPG/2021/003086-G is highly acknowledgement. We acknowledge Prof. Vikas Jain from Department of Biological sciences from IISER Bhopal for doing MD simulation studies. Manika Sharma is thankful to the University Grant Commission (UGC) for providing Junior Research Fellowship (JRF). Parul Bhatt is Research Scientist-I in ICMR funded project (EMDR/IG/10-2023-0001022). References Agger, E. M. (2016). Novel adjuvant formulations for delivery of anti-tuberculosis vaccine candidates. In Advanced Drug Delivery Reviews (Vol. 102, pp. 73–82). Elsevier B.V. https://doi.org/10.1016/j.addr.2015.11.012 Al Tbeishat, H. (2022). Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. Scientific Reports , 12 (1). https://doi.org/10.1038/s41598-022-08506-4 Behzadi, P., García-Perdomo, H. A., & Karpiński, T. M. (2021). 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Bioengineering and Translational Medicine . https://doi.org/10.1002/btm2.10709 Zhu, Y., Shi, J., Wang, Q., Zhu, Y., Li, M., Tian, T., Shi, H., Shang, K., Yin, Z., & Zhang, F. (2024). Novel dual-pathogen multi-epitope mRNA vaccine development for Brucella melitensis and Mycobacterium tuberculosis in silico approach. PloS One , 19 (10), e0309560. https://doi.org/10.1371/journal.pone.0309560 Additional Declarations No competing interests reported. Supplementary Files Supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6579802","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":530204592,"identity":"cfe7badd-9bd0-4273-800d-ad183861a170","order_by":0,"name":"Manika Sharma","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Manika","middleName":"","lastName":"Sharma","suffix":""},{"id":530204593,"identity":"d83cafb3-a979-4218-955a-35454ad87a7c","order_by":1,"name":"Parul Bhatt","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Parul","middleName":"","lastName":"Bhatt","suffix":""},{"id":530204594,"identity":"142eba3e-71b3-43d6-8e59-09656630166c","order_by":2,"name":"Medha Singh","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Medha","middleName":"","lastName":"Singh","suffix":""},{"id":530204595,"identity":"d2b58ec0-8b46-4353-8531-2654fbd72c7f","order_by":3,"name":"Monika Sharma","email":"","orcid":"","institution":"University of Delhi","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Sharma","suffix":""},{"id":530204596,"identity":"d956f6b2-1b31-434d-abcf-600d8e22b016","order_by":4,"name":"Sadhna Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABOUlEQVRIiWNgGAWjYHACMzBpACYrGHhAlARjA1xagoCWMyRrYWyDKkPSggHk2w9ve1xQw2Bvzn726Yaf87bJmLefPXjj5w6bxPntDYwffjBY5KFpMTiTVm484xhD4s6edLObvdtu88icyUu27D2TlrjhzAFmyR4GiWJ0LQw5ZtI8bAwJBgfS2G7wArVIAEUkeNsOJ26QSGCQBjoyEc2R8v1vgFr+MdgbnH/GdvPvHKAW/jdmkn+BWubPf8D8G4sWhhtAW3jbGBg33Ehju83bANQiARY5nNhwg4ENmy0GN56VSfP2SSRuuPGM7bbMMZCWN8bWsm1pxhvOJLZZ9hhgcVjyNmmebzZAh6Wx3XxTc9tegj/H8ObbNhvZ+e2HD9/4UVGH4TBoRGAVBcWOAVaZUTAKRsEoGAX4AQAHZnCl8ZgXfgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Delhi","correspondingAuthor":true,"prefix":"","firstName":"Sadhna","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-05-02 16:23:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6579802/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6579802/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93742232,"identity":"588e5f5c-235e-49db-8334-1b3d1b232443","added_by":"auto","created_at":"2025-10-17 05:35:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":179751,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic workflow of the study:\u003c/strong\u003e The study was formulated with the selection of proteins, retrieval of their respective sequences and their \u003cem\u003ein-silico\u003c/em\u003echaracterisation. Shortlisted peptides were utilized for designing of mRNA vaccine by inserting the required structural nucleotides. Docking studies of translated peptide with TLR2 and TLR4 was performed for designed vaccine. Simulation of immune response against vaccine was also evaluated.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/d39b5e8ca67e82084a7c0a83.jpeg"},{"id":93741961,"identity":"745da8ee-52b6-40ba-bc6f-1c9323231e1a","added_by":"auto","created_at":"2025-10-17 05:27:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":285876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal Population coverage of epitopes is 99.98%:\u003c/strong\u003e World map indicating the population coverage of alleles selected to design vaccine. Countries shown in grey indicate unavailability of data whereas the dark magenta color predicts higher distribution of alleles.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/546cdec6fa1abf50b1657a1c.png"},{"id":93741959,"identity":"6ed1057f-2b77-49a4-ae7f-11ba79b17684","added_by":"auto","created_at":"2025-10-17 05:27:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003emRNA structure of the designed vaccine: \u003c/strong\u003eDesigned structure of mRNA constituting of 5'm\u003csup\u003e7\u003c/sup\u003eG cap and β-globin (green color) at 5' end followed by pink color kozak sequence and tissue plasminogen activator (blue color). The coding sequence of Mce peptide is in dark blue rectangular block. Selected epitopes from stage specific proteins were shown in violet, orange and red color. Black and blue vertical lines are the KPKPKP and AAY linkers. MHC I targeting domain (blue color), α-globin (green trapezium) and Black bold vertical Poly (A) tail located at 3' end.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/f0f292de74ee7e5bd7d316df.jpeg"},{"id":93742235,"identity":"60d3e4f8-cc9c-4035-9016-3bb2ce88e891","added_by":"auto","created_at":"2025-10-17 05:35:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":227235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSecondary structure of designed mRNA vaccine construct and mountain plot generated by RNAwebfold sever:\u003c/strong\u003eA)Secondary structure of designed mRNA vaccine showing MFE base pair probabilities where 0 (purple) depicts lowest and 1(red) depict highest base pair probability. B) Centroid secondary structure displaying base-pair probabilities. C) Mountain plot depicting sequence position versus the number of base pair that enclose that position. Red, green and blue lines represent Minimum free energy, Pair probabilities and centroid structure respectively.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/7500ce9f6c413f066bacab00.png"},{"id":93741960,"identity":"8c9167d3-445f-4b16-b1c0-fcb82c542324","added_by":"auto","created_at":"2025-10-17 05:27:40","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":49237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn-Silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Cloning\u003c/strong\u003e: \u003cem\u003eIn-silico\u003c/em\u003ecloning methodology in Snap gene.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/bdddc0b2de5ca7f5ce9c67d8.jpeg"},{"id":93741962,"identity":"6db824e4-c69a-4988-b6b9-1569f5e5b3e4","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural Validation of Vaccine Construct\u003c/strong\u003e: A) Visualization of 3Dmodel structure of vaccine construct where red region\u003cstrong\u003e \u003c/strong\u003erepresents the helical region, green represents the β sheet and the grey color represents the coil area. B) Ramachandran plot of the selected model of vaccine construct.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/fc0b1469a49a0f6318b016f5.jpeg"},{"id":93741967,"identity":"6f4abb2d-2f46-46f0-bd79-9a59825d3d0d","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":93162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Results of Secondary Structure of Vaccine Construct;\u003c/strong\u003eGraphical results of Secondary structure prediction of the designed vaccine by GOR4 server. Blue, red and purple lines indicate helix, coil and strand structure respectively.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/4c7cb7213d9baeab13160b1d.jpeg"},{"id":93741966,"identity":"26f31259-73c4-4123-8fe3-40292db3356f","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":239895,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDocking Structure of Vaccine Construct With A) TLR2 And B) TLR4\u003c/strong\u003e: Designed vaccine construct was docked with immune receptors (TLR2 and TLR4) a) TLR2 shown in yellow CPK model and vaccine construct in red. b) Vaccine construct binds into the groove of TLR4 (blue).\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/f829e0abd7231f1ea0e54619.jpeg"},{"id":93741965,"identity":"62f47de0-cf13-4533-a977-611d2bad15f5","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":166608,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular dynamic simulation of translated mRNA vaccine with TLR2 complex.\u003c/strong\u003e The molecular dynamic simulation was performed using Gromacs software (a) represents Root Mean Square Deviations (RMSD) trajectory values of translated mRNA vaccine construct with TLR2. (b) represents Root Mean Square Fluctuation (RMSF) of the protein alpha chain of TLR2 with translated mRNA vaccine.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/3593e2d77371b004e24ca6e7.png"},{"id":93741969,"identity":"6d48a293-ae85-419e-8c08-834c27fd82fd","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":632315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune simulations response prediction by C-Immsim server:\u003c/strong\u003e Immune simulation prediction response by double dose of vaccine injection using C-Immsim server. a) T-helper cell population graph showing memory cells enlasting for long duration. b) Percentage and volume of Th1 type immune cells generation with vaccine injection lasting for several months. c) T-cytotoxic cells simulation response showing active phase cells. d) Number of macrophages generated after vaccine injection. e) IFN-γ and Interleukin-2 show heightened response than other visualized cytokines. f) Level of antigen specific antibody generated in response to vaccine injection.\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/b2c6245cfcc9c0d5e761cfc1.jpeg"},{"id":93742740,"identity":"caf7c44d-a14b-44d7-9ca9-6c74143bd2e6","added_by":"auto","created_at":"2025-10-17 05:43:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3430231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/98a54d33-e67a-4945-bebe-6bd9151f1458.pdf"},{"id":93741964,"identity":"fd88405d-6b2e-41bd-a609-a85fcb91b7ec","added_by":"auto","created_at":"2025-10-17 05:27:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29659,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6579802/v1/4c8f9aede4980f9d10c42f4b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Design and validation of multi-stage expressing mRNA vaccine for Mycobacterium tuberculosis through computational technology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMillions of people worldwide are affected by Tuberculosis (TB), which is caused by the bacterium\u0026nbsp;\u003cem\u003eMycobacterium\u0026nbsp;tuberculosis\u003c/em\u003e (Mtb). According to WHO report 2024 approximately 10.8 million individuals were infected and 1.9 million died from this infectious disease (WHO, 2024). Treatment is time-consuming and inefficient due to the emergence of drug-resistant strains (Nguyen, 2016) \u003cspan lang=\"EN-IN\"\u003e(Glynn et al., 2002)\u003c/span\u003e. A potential post exposure vaccine is a promising approach for reducing antibiotic resistance and impeding the global spread of disease(Hasso-Agopsowicz et al., 2024) (Garbuglia et al., 2020) (Frost et al., 2023). Currently, Bacillus Calmette-Gurein (BCG) is the only licensed prophylactic vaccine which prevents TB infection in paediatric patients but is ineffective in adults. During the COVID-19 pandemic, mRNA vaccine was given emergency authorization and are now being synthesised for other infectious disease. For many viral infections like HIV, dengue, Zika, influenza; mRNA vaccines are undergoing different stages of clinical trials (Jain et al., 2020). Some mRNA vaccines for TB have also undergone immunological confirmation at \u003cem\u003ein silico\u003c/em\u003e stage and only one \u003cem\u003ein vivo\u003c/em\u003e stage (Al Tbeishat, 2022; Larsen et al., 2023; Shahrear \u0026amp; Islam, 2023; Zhu et al., 2024). The continuous efforts of more than two decades have resulted in 12 TB vaccine candidates under active clinical trials as of September 2024 with only two mRNA vaccines BNT164a and BNT164b (WHO, 2024). All these TB vaccines under trial constitutes of early stage expressing whole proteins, viral vector or live attenuated/killed whole Mtb based formulations. On the other hand, several subunit vaccines are under advanced clinical trials, but these are having low immunogenic activity(Schrager et al., 2020). The low immunogenicity of protein\u0026nbsp;subunit vaccines and use of extensive cell culture and intricate purification schemes makes them less attractive that are frequently protein specific. The mRNA vaccines, on the other hand, are quick to produce, scalable and cost-effective because they can be synthesized in a single cell-free reaction and have a standard purification process, regardless of the antigen sequence (Kis et al., 2020) (Park et al., 2021). Moreover, subunit vaccines necessitate use of adjuvants\u0026nbsp;such as\u0026nbsp;AS01, CAF01, GLA-SE,\u0026nbsp;and\u0026nbsp;IC31(Agger, 2016).\u0026nbsp;These adjuvants have some inflammatory and reactogenic issues that need to be resolved\u0026nbsp;\u003cspan lang=\"EN-IN\"\u003e(Stewart et al., 2019)\u003c/span\u003e. mRNA vaccine can generate better immune response even in the absence of adjuvant.\u0026nbsp;Few studies compared the adjuvanted subunit vaccine and mRNA vaccine and found that mRNA vaccine induce both humoral and cellular immune response against the pathogen \u0026nbsp;(Lin et al., 2023)\u0026nbsp;(Monslow et al., 2020)\u0026nbsp;(Wu et al., 2022). The use of mRNA vaccine has other beneficial features such as low infection risk, high stability, translational ability, amplifiable \u003cem\u003ein-vivo\u003c/em\u003e half-life, efficient delivery and expression in the host (Kariko et al., 2008) (Kauffman et al., 2016) (Gaun et al., 2017) (Thess et al., 2015).\u0026nbsp;Thus, it directed us to design an mRNA vaccine for TB instead of a peptide-based vaccine.\u003c/p\u003e\n\u003cp\u003eOne important aspect we considered while designing vaccine is the multistage nature of Mtb infection where it can either actively replicate causing disease symptoms or remain dormant causing latent infection(Bellini et al., 2023; Yang et al., 2024). Therefore, the selection of antigens was crucially done which could generate protective response against different stages of TB. Among the early stage expressing antigens of Mtb, three proteins were selected MPT64 (Rv1980c), ESAT-6 (Rv3875), and CFP10 (Rv3874). From the late-expressing proteins, two proteins were from dormancy associated regulon DosR family, (Rv1738 and Rv1733c) and one from the Glycosyl hydrolases family 65 (Rv2006). The constitutively expressed protein from PE-PGRS family included was WAG22 (Rv1759c). Among 16 Mammalian Cell Entry (Mce) operon proteins, Mce4A and Mce4F proteins, which are specifically known for host cell invasion were also incorporated in the vaccine\u0026nbsp;\u003cspan lang=\"EN-IN\"\u003e(Saini et al., 2008)\u003c/span\u003e(F. Zhang \u0026amp; Xie, 2011)(Rodr\u0026iacute;guez et al., 2015).\u0026nbsp;\u0026nbsp;We expect that this vaccine construct can fight Mtb at different stages of infection.\u003c/p\u003e\n\u003cp\u003eDuring infection, the interaction of bacterial pathogen associated molecular patterns (PAMPs) with the host pattern recognition receptors (PRRs) is the prime step that activate various immune cells (Mogensen, 2009). Among the various classes of PRRs, Toll-like receptors (TLRs) are key players in elicitation of the immune response against Mtb specifically TLR2 and TLR4 (Heldwein \u0026amp; Fenton, 2002)(Pattanaik et al., 2022).\u0026nbsp;Activated immune cells further process and present the Mtb derived antigenic peptides and mount them on Major Histocompatibility Complex (MHC) molecules for priming of T lymphocytes. CD4+ helper T cells are primed by MHC class II peptide epitopes while CD8+ cytotoxic T cells are primed by MHC class I peptide epitopes (Furuta et al., 2012) (Li et al., 2013). \u0026nbsp; Primed T cells lead to release of repertoire of pro-inflammatory cytokines which are implicated in macrophage activation (Bettencourt et al., 2020; Chai et al., 2020; Khan et al., 2021).\u003c/p\u003e\n\u003cp\u003eIn this study, we have employed an immuno-informatics approach for designing a novel mRNA vaccine for the multi-stage TB infection. The translated product of mRNA vaccine has eighteen promiscuous antigenic epitopes as described above which are strong binders of class I and class II MHC alleles. Also, these epitopes are pro-inflammatory, non-toxic, non-allergenic with wide population coverage. The translated product of mRNA vaccine was also predicted to have strong binding affinity for TLR2 and TLR4 receptors. Furthermore, simulation studies predicted the elicitation of strong Th1 type protective immune response for our vaccine construct with memory T cells lasting for months and secretion of pro-inflammatory cytokines.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Schematic work plan for designing mRNA vaccine-\u003c/strong\u003e A snapshot of \u003cem\u003ein-silico\u003c/em\u003e work plan for designing of mRNA vaccine is depicted in Figure1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Selection of promiscuous antigenic epitopes for mRNA vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrieved protein sequences were submitted to NetMHC4.0 server for predicting MHC I restricted CD8 T-cell epitopes and in IEDB server for MHC class II restricted CD4 T-cell epitopes (Lundegaard et al., 2008)(Nielsen et al., 2007). NetMHC4.0 server adopts Artificial Neural Network (ANN) algorithm with 81 different Human Leucocytes Antigens (HLA) alleles. Peptide length of 9-mer was selected with recommended threshold value of 0.5% for strong binders and 2% for weak binders. IEDB server is based on Stabilization Matrix Method (SMM) align and evaluates the binding affinity of 15mer peptides for the 15 documented HLA-DR alleles based on the Inhibitory Concentration (IC50) value. Lower IC50 value is proportional to high binding affinity between peptide-MHC complex (Fleri et al., 2017). Strong binders were selected with default IC50\u0026le;250 score. Characterization of promiscuous epitopes was done based on their binding affinity to three or more alleles of class I, class II or both. \u0026nbsp;All the promiscuous epitopes were analysed for their antigenicity through Vaxijen2.0 sever where a score of \u0026gt;0.4 was considered antigenic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Elimination of cross-reactive peptide\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe promiscuous antigenic epitopes were analysed against the annotated human proteins. NCBI protein blast (https://blast.ncbi.nlm.nih.gov/Blast.cgi) search was done for the selected peptide epitopes against human genome. The peptides which were found to be 100% identical with the human genome were excluded from further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Evaluation of pro-inflammatory, non-allergenic and non-toxic nature of selected epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Pro-inflammatory peptides that can induce IFN-\u0026gamma;, TNF-\u0026alpha;, IL-12, IL-18 and IL-8 cytokines were selected using PIP-EL server. AIPpred server was used for evaluating the anti-inflammatory nature of selected promiscuous, antigenic peptide epitopes(Manavalan et al., 2018a). Epitopes which were having more PIP-EL score than AIPpred score obtained were considered pro-inflammatory. The allergenicity and toxicity of the promiscuous antigenic pro-inflammatory peptides were determined by AllerTOPv2.0 and Toxin-Pred server. \u0026nbsp;AllerTOPv2.0 predicts the allergenicity of sequence based on physicochemical properties (Dimitrov et al., 2014). The promiscuous antigenic epitopes that were non allergen were then submitted to Toxin-Pred server to analyse the toxicity of the peptides. The peptides with score 0.0 were selected and the peptides with more than 0.0 were considered toxic and excluded from the study \u0026nbsp;(Gupta et al., 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Population coverage of selected epitopes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is wide range of diversity in MHC alleles depending upon population geography and ethnicity. Analysis of population coverage was done by IEDB population coverage tool with the selected epitopes binding to both MHC class I and MHC class II alleles (http://tools.iedb.org/population/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Designing of mRNA vaccine construct\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe shortlisted epitopes for vaccine construct were promiscuous, antigenic, non-allergenic, non-toxic and pro-inflammatory. The vaccine construct was designed by incorporating these epitopes using the linkers. Since our aim was to design mRNA vaccine therefore to make a stable mRNA vaccine following modifications were incorporated in the mRNA sequence.\u003c/p\u003e\n\u003col class=\"decimal_type\" style=\"list-style-type: upper-alpha;\"\u003e\n \u003cli\u003eAddition of 7-methyl guanosine sequence was done at 5\u0026apos; end.\u003c/li\u003e\n \u003cli\u003eSubsequently RNA sequence of \u0026beta;-globlin was attached after 7-methyl guanosine as 5\u0026apos; Untranslated region (UTR).\u003c/li\u003e\n \u003cli\u003eKozak Sequence was also incorporated after 5\u0026apos; UTR region to enhance the translation initiation efficiency.\u003c/li\u003e\n \u003cli\u003eFollowed by Kozak sequence, Tissue plasminogen activator (tPA) signal sequence was attached to enhance immunogenicity (Kou et al., 2017a) (Uniprot ID-P00750).\u003c/li\u003e\n \u003cli\u003eSignal sequence of MHC-I targeting domain (MITD) at 3\u0026apos; end to target the CTL epitopes to MHC I region of endoplasmic reticulum (Kreiter et al., 2008) (Uniprot ID-Q8WV92).\u003c/li\u003e\n \u003cli\u003eSequence of \u0026alpha;-globlin was added after MITD sequence as 3\u0026apos; UTR.\u003c/li\u003e\n \u003cli\u003ePoly (A) tail that works synergistically with 5\u0026apos; cap to stabilize the mRNA was also linked at 3\u0026apos; end.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Stability and secondary structure prediction of designed mRNA vaccine\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stability of the mRNA vaccine was estimated using RNAfold webserver (Gruber et al., 2015). This server also predicts the secondary structure of mRNA. Loop based energy model and dynamic programming algorithm was employed for determination of secondary structure. This server predicts the thermodynamic stability of mRNA and generates a minimal free energy score (MFE). Furthermore, mountain plot was also obtained. This plot is combinatorial graph of MFE, centroid structure and pair probabilities. The centroid mRNA structure gives the minimal base pair distance to all other secondary structure in the Boltzmann ensemble. MFE based secondary structure defines functional motifs of mRNAs which further determine the thermodynamic properties. All these parameters of our mRNA vaccine construct were compared with wild retinoblastoma (Rb1) mRNA taken as a positive control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Codon optimisation and \u003cem\u003eIn\u003c/em\u003e-\u003cem\u003esilico\u003c/em\u003e cloning of mRNA vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe designed construct was further subjected to Java codon adaptation tool (JCAT) server for codon optimization in \u003cem\u003eE.coli\u0026nbsp;\u003c/em\u003estrain K12 (Grote et al., 2005). The sequence predicted by JCAT server was employed for in silico cloning using Snap Gene server in pET28:GFP vector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Homology modelling and structural validation of vaccine construct\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA vaccine construct was translated into protein and the translated protein was modelled in I-TASSER server (Roy et al., 2010). The C-score depicts the accuracy of model; considering the value between -5 and 2, the higher C value signifies the higher stability of the model. Five models in ITASSER were generated and subjected to Chiron server for energy minimization. Best structure was selected through structural validation in PROCHECK, ERRAT and VERIFY 3D web server available in UCLA SAVES. In PROCHECK, the stereo-chemical nature of structure is predicted via Ramachandran plot (Laskowski et al., 1996). ERRAT and VERIFY 3D evaluates summative quality factor and the best comparative structure of the model respectively (Colovos \u0026amp; Yeates, 1993).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Physicochemical properties of translated vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExPaSy Database server ProtParam was used to analyse the physicochemical properties of the translated vaccine construct which predicted sequence length, molecular weight, stability index, grand average of hydropathicity (GRAVY) and half-life. Furthermore, the allergenicity, toxicity and pro-inflammatory nature of the whole construct were also predicted as described previously (Dimitrov et al., 2014) (Manavalan et al., 2018b) (Gupta et al., 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Secondary Structure of construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetails of secondary structure of translated vaccine construct was predicted by the GOR (Garnier-Osguthorpe-Robson) IV sever (Sen et al., 2005). GOR server is based on information method where information\u0026rsquo;s generated by each residue were combined to predict function of a protein.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Docking of translated vaccine construct with TLRs immune receptors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe binding affinity of the translated vaccine construct with TLR2 and TLR4 was estimated through PATCHDOCK and FIREDOCK severs (Schneidman-Duhovny et al., 2005)(Mashiach et al., 2008). Binding energy score between vaccine construct and receptor was generated by Hex8.0 server and the interactions were visualized by Discovery Studio 4.0. The various LRR regions in the receptor residues binding with specific amino acid residue of vaccine construct was also visualized by Discovery Studio 4.0 (https://discover.3ds.com/discovery-studio-visualizer-download).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.14 Molecular dynamic simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe docked complex of the translated mRNA construct and TLR2 was then subjected to MD simulation using Gromacs software 2020.5(Refianti et al., 2011). Using\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eCHARMM36 force field, the docked complex was solvated by TIP3P (transferable intermolecular potential with 3 points) water model(Kern et al., 2024). Structures generated were then run for MD simulation at 300K temperature and standard pressure of 1.01 bar using NPT (number of particles [N], system pressure [P] and temperature [T]) and NVT (number of particles [N], system volume [V] and temperature [T]) ensemble. The trajectory was then analysed by Root Mean Square Fluctuation (RMSF) and Mean Square Deviation (RMSD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 \u003cem\u003eIn-silico\u0026nbsp;\u003c/em\u003eImmune simulation studies of the translated vaccine construct\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC-ImmSim server predicts the immune response evoked by any protein (Rapin et al., 2010). The protocol in C-Immsim was defined with two injections of 50 \u0026micro;l each within intervals of 4 weeks. First dose was set as one and the booster dose at 84-time steps. The time step selected to be 1000 and 12,345. LPS was precluded from the injection. \u0026nbsp;Although we identified only T-cell epitopes to make mRNA vaccine, we also searched for B-cell epitopes. \u0026nbsp;ABCpred software with threshold of 0.51 was utilised to find the same.\u0026nbsp;\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSelection of promiscuous antigenic and proinflammatory epitopes for mRNA vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll nine selected proteins were passed through MHC class I and MHC class II epitope prediction software where 714 and 134 promiscuous antigenic epitopes were obtained respectively. Out of 87 overlapping promiscuous antigenic epitopes which bind to both MHC class I and MHC class II alleles, 18 epitopes were selected after removing self-peptide. There antigens were further evaluated for being pro-inflammatory, antigenic, nontoxic and non-allergenic properties (Table S1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u003c/strong\u003e \u003cstrong\u003eEvaluation of\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;pro-inflammatory, anti-allergenic and non-toxic nature of selected epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 18 selected promiscuous antigenic epitopes, all were found to be pro-inflammatory in nature. TSVRVAAAL epitope have highest pro-inflammatory score 0.6328 whereas LVVFPALFA have lowest pro-inflammatory score 0.4998 among the selected peptides. All the promiscuous antigenic epitopes were found to be anti-allergenic and non- toxic in nature. These epitopes were used for construction of mRNA vaccine. Table S2 enlists the characteristic scores obtained for selected epitopes along with binding profile to both MHC alleles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003cstrong\u003ePopulation coverage of shortlisted epitopes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT cell recognized epitope in a population with a specific HLA composition may not elicit response in another population with different HLA composition. Therefore, it becomes essential to select epitopes that bind to numerous HLA supertypes thereby ensuring broad coverage across different populations. Selected epitopes corresponding to their respective alleles have been studied for their population coverage percentage in the different countries. Figure 2 depicts the specific coverage region of the selected epitopes. The global coverage stands for 99.98% which entails quite high allelic distribution across the world. Almost all the regions showed population coverage in the range of 97-99.9%\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDesigning of mRNA vaccine construct\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mRNA vaccine construct consisted of following elements sequenced from N terminal to C terminal:\u003c/p\u003e\n\u003cp\u003e5\u0026apos;m\u003csup\u003e7\u003c/sup\u003eG cap - 5\u0026apos;UTR (\u0026beta; globin) \u0026ndash; Kozak sequence \u0026ndash; tPA (Signal peptide) \u0026ndash; Mce peptide epitopes \u0026ndash; KPKPKP linker \u0026ndash; Early, Late and Consistent expressing protein epitopes linked via AAY linker \u0026ndash; MITD signal sequence - 3\u0026apos;UTR (\u0026alpha; globin) \u0026ndash; Poly (A) tail (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Evaluating the stability of mRNA vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stability of secondary structures of our designed mRNA construct was evaluated by RNAfold web server and it predicted that our secondary structure has optimal free energy of -1688.60 kcal/mol which is more than wild Rb1 mRNA having -1154kcal/mol free energy \u003cspan lang=\"EN-IN\"\u003e(Uddin et al., 2015)\u003c/span\u003e. Interestingly, our designed mRNA construct consists of -1141.82 kcal/mol of centroid free energy which is more than free energy (-827.99kcal/mol) generated by Rb1 mRNA. Thermodynamic ensemble energy of our designed mRNA vaccine was also comparable to the wild Rb1 mRNA. The mountain plot also confirms the stability of the designed mRNA and predicts that it remains stable after manufacturing (Figure 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Codon optimisation and \u003cem\u003ein-silico\u003c/em\u003e cloning of mRNA vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimised codon sequence has 696 nucleotides. The codon adaptation index (CAI) value was predicted 1.0, and the average GC content of the adapted sequence was 55.2%, which indicates high expression in the \u003cem\u003eE. coli\u003c/em\u003e host. Finally the adapted codon sequence was \u003cem\u003ein silico\u003c/em\u003e cloned in pET28:GFP vector (Figure 5) using snap-gene server.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Structural validation of the translated vaccine using I-TASSER\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI\u0026ndash;TASSER generated 5 tertiary structures of the construct; based on all analyzed parameters model 1 (Figure 5 (A)) with C-score of -1.85 was selected. The overall quality factor was 86.6% in ERRAT and VERIFY 3-D score was 39.66%. Ramachandran plot shows 71.6% residues residing in favored region, 20.5% residues in generously allowed regions and 3.3% residues in disallowed regions (Figure 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Physiochemical Properties of translated vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe translated vaccine construct is 232 amino acid sequence with molecular weight of 24.59kDa. The theoretical PI score of 9.38, indicating the construct to be alkaline in nature. Half- life of the designed construct is \u0026gt;100 hours (mammalian reticulocytes, \u003cem\u003ein vitro\u003c/em\u003e), \u0026gt; 20hrs in yeast (\u003cem\u003ein vivo\u003c/em\u003e) and \u0026gt;10 hrs. in \u003cem\u003eE.coli\u0026nbsp;\u003c/em\u003e(\u003cem\u003ein vivo\u003c/em\u003e) implicating the structure is stable in vivo. The instability index was computed to be 30.56 classifying our protein to be stable. The Grand Average of Hydropathy (GRAVY) and aliphatic score were estimated to be 0.604 and 30.56 respectively. The translated construct was predicted to be non- allergenic, antigenic with a score of 0.6465 and pro-inflammatory in nature with PIP score 0.6624.\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003e3.9 Predicting secondary structure of the vaccine construct\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe secondary structure of the Construct in GOR4server predicts 81.8% of residues in the alpha helix region, 16.33% forms random coil structure and 1.72% of residues forms extended strand structure (Figure 7).\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003e3.10 Translated vaccine construct show strong interactions with TLRs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInteraction of ligands with TLRs activate signaling cascade that are essential for the efficacy of vaccine. So, the structurally validated designed vaccine model was docked with TLR2 and TLR4 to evaluate their affinity towards the immune receptors. Our translated version also showed strong affinity for TLR2 as well as TLR4 receptors. Vaccine construct-TLR2 docked complex showed 13 hydrogen bond interactions and high energy score of -859.56kcal/mol (Figure 7a). LprG, a known TLR2-agonist was included as a positive control and showed a comparative docked energy score of -832.93kcal/mol with TLR2. Interestingly, our vaccine construct facilitated strong interactions involving the LRR regions of TLR2. There were 20 bonds (11 H-bonds, 2 electrostatic bonds and 7 hydrophobic bonds) out of the total 26 interactions that were mediated with various LRR regions of TLR2 (S3). Vaccine construct-TLR4 docked complex showed even better binding affinity evident by high Hex energy score of -933.50kcal/mol. The rpfB protein of Mtb which is reported to be TLR4-agonist showed an energy score of -718.95kcal/mol with TLR4 (Figure 8b). Additionally, there were 9 LRR regions of TLR4 involved in bonding interactions with our vaccine construct.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.11 MD simulation of docked complexes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD simulation was performed with the stable docked structure of the translated mRNA vaccine and TLR2 for 100ns. RMSD analysis of the trajectory shows that the complex backbone initial increased for 5ns and then reaches a stable state with an average value of 0.4nm to 0.6nm for 100ns (Figure 9a). The average RMSF value of docked complex was 0.30nm (Figure9b). These findings suggest that complex structures have higher rigidity and are more stable with a low variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.11 Evaluation of immune response generated through C-ImmSim server\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mammalian immune response was evaluated by C-ImmSim server. There is a significant increase of pro-inflammatory IFN-\u0026gamma;, IL-12, IL-2 with the booster administration of our designed vaccine (Figure 10). The T- cells were shown to generate quite a high number of memory cells persisting for several months. Although, designed as T-cell specific peptide-based vaccine, it is also predicted to evoke humoral immune response evident by secretion profile of IgG1 class antibodies. Also, the designed vaccine was able to generate antigenic B-cell epitopes in ABCpred software. Thus, exposure of our vaccine can generate both humoral and cell-mediated immune response. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eIn-silico\u003c/em\u003e methods have created momentum for vaccine designing in the scientific community and have accelerated the possibility to attain the goal of \u0026lsquo;End TB Strategy\u0026rsquo; by 2030. Most of the antigens selected as vaccine candidates and under pre-clinical trials are early expressing antigens of Mtb. Considering the infection spectrum of Mtb, we have selected a combination of nine antigenic proteins. Among these, early expressing proteins (ESAT-6, CFP-10 and MPT-64), constitutively expressing protein (Wag22), proteins expressing at dormant phase (Rv2006, Rv1738 and Rv1733c) and proteins helping the entry of pathogen into the host (Mce4A and Mce4F), were found to be highly immunogenic.\u0026nbsp;\u003cspan lang=\"EN-IN\"\u003e(Brandt et al., 2000)\u003c/span\u003e(Passos et al., 2024)(Valizadeh et al., 2022) (Campuzano et al., 2007) \u003cspan lang=\"EN-IN\"\u003e(Murphy et al., 2005)\u003c/span\u003e (Kassa et al., 2012) (Zvi et al., 2008)(Saelee et al., 2022) (W. Zhang et al., 2014) (Hemati et al., 2019). All these nine antigenic proteins were used to generate 18 promiscuous antigenic epitopes\u0026nbsp;having strong\u0026nbsp;binding affinity for both MHC class I and class II alleles. It is required for generation of effective cell-mediated immune response\u0026nbsp;which play protective role against TB pathogenesis and bacterial clearance (Jasenosky et al., 2015).\u0026nbsp;Incorporation of AAY and KPKPKP linkers in our mRNA vaccine candidate is predicted to\u0026nbsp;enhance immunogenicity (Wang et al., 2004) (Jafari et al., 2020).\u003c/p\u003e\n\u003cp\u003eThe translational efficiency and half-life of mRNA vaccine candidate is increased by addition of poly(A) tail, human \u0026beta;-globin gene at 5\u0026prime; UTR and human \u0026alpha;-globin gene at 3\u0026prime; UTR\u003cspan lang=\"EN-IN\"\u003e(Passmore \u0026amp; Coller, 2022)\u003c/span\u003e (Ma et al., 2024). Human \u0026beta;-globin gene help in stabilizing ribosomal binding at 5\u0026rsquo;UTR and \u0026alpha;-globin gene help in effective termination of translation at 3\u0026rsquo;UTR (Ryczek et al., 2023). MITD sequence incorporated in our vaccine construct is known to stimulate the antigen-specific immune response. This stimulation is because of MITDs ability to mimic the natural transport characteristics of MHC I molecules. Due to the cross presentation, MITD sequence was found to stimulate both CD4+ and CD8+ cells\u0026nbsp;(Y. Zhang, Zhai, Huang, et al., 2024)(Zhang, Zhai, Qin, et al., 2024). The tPA signal sequence targets mRNA to endoplasmic reticulum and improves immunogenicity\u0026nbsp;(Zhang, Zhai, Qin, et al., 2024)(Kou et al., 2017b).\u0026nbsp;The free energy and centroid score of our mRNA vaccine was higher as compared to human Rb1 mRNA that indicates the mRNA vaccine is stable in human body.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur designed mRNA vaccine is translated, the stability and immunological profile was evaluated for effective immune response. The computational analysis of the translated mRNA vaccine predicted that the vaccine is highly antigenic, pro-inflammatory, non-toxic, and thermostable with longer half-life in the human reticulocyte, making it as a possible mRNA vaccine candidate. The codon optimisation of the mRNA vaccine showed 55.2% GC content and CAI value 1 indicates its efficient expression in the host(Jin et al., 2025). Our vaccine\u0026rsquo;s secondary structure analysis revealed that 81.8% of helical regions can add rigidity, compactness and facilitate better interaction with host immune receptors (Bromley \u0026amp; Channon, 2011). \u0026nbsp; More the negative energy score between vaccine and receptors (TLR2 and TLR4) suggests the high binding affinity. LRRs specific interaction are important for activation of TLRs signalling and generation of innate immune response(Behzadi et al., 2021). Through molecular docking analysis translated vaccine construct was found to interact with TLR2 and TLR4 within LRRs specific regions. The interaction of translated mRNA vaccine with TLR4 was verified using MD simulation.\u003c/p\u003e\n\u003cp\u003eThe i\u003cem\u003en-silico\u003c/em\u003e immune simulation predicted robust enhancement of immune response generated by dual dose administration of vaccine. Our vaccine construct was found to generate cell mediated immune response against tuberculosis which is crucial in controlling the infection and observed high tighter of antibody generation.\u0026nbsp;The cationic peptide incorporated in the vaccine may be helpful in generation of humoral immune response along with cellular immune response\u0026nbsp;(Fritz et al., 2004). The combinatorial increase of B and T- cells activity lasting for long time, along with the production of pro-inflammatory cytokines and activated macrophages substantiates it to be a promising vaccine candidate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough mRNA vaccine has several advantages, but they also have limitations including their instability on storage and delivery. To overcome this\u0026nbsp;mRNA vaccine can be delivered by encapsulating with lipid nano particle or dendritic cell (DC) membrane coated nanoparticles for targeted delivery(Hou et al., 2021)\u0026nbsp;(Cao et al., 2023).\u0026nbsp;This delivery system can be administered intramuscularly leading to potential adaptive immune system activation by recruiting various DC subsets which can prime T and B cell(Y. Zhang, Zhai, Qin, et al., 2024)(Liang et al., 2017). In this study, we identified dominant T cells epitopes through a series of computational analyses and converted them into multi-epitope mRNA vaccine. The designed vaccine exhibits favourable immunodominant characteristics and solubility. Using molecular docking, and \u003cem\u003ein silico\u003c/em\u003e cloning, it was found that the mRNA vaccine-TLR2and TLR4 have stable interaction. Once transported \u003cem\u003ein vivo\u003c/em\u003e it can be expressed into protein. This protein is predicted to be stimulating prolonged cellular and humoral immunity with broad population coverage. Based on our findings, we believe that the vaccine candidate could be a starting point for the development of effective vaccines targeting different stages of Mtb infection. Furthermore, it is essential to conduct future experimental studies initially on PBMCs of patient and then on pre-clinical murine models to confirm the immunogenicity of this mRNA vaccine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProf. Sadhna Sharma:\u0026nbsp;\u003c/strong\u003eConceptualization,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eMethodology, Software,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData curation, Visualization, Validation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Monika Sharma:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Visualization, Validation, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eManika Sharma:\u0026nbsp;\u003c/strong\u003eMethodology, Software,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eData curation, Visualization, Validation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParul Bhatt:\u0026nbsp;\u003c/strong\u003eMethodology, Software,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eVisualization, Validation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedha -\u0026nbsp;\u003c/strong\u003eMethodology, Software, Data curation, Visualization, Formal analysis, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that the authors have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding by Department of Science and Technology (DST) SERB POWER grant SPG/2021/003086-G is highly acknowledgement. We acknowledge Prof. Vikas Jain from Department of Biological sciences from IISER Bhopal for doing MD simulation studies. Manika Sharma is thankful to the University Grant Commission (UGC) for providing Junior Research Fellowship (JRF). Parul Bhatt is Research Scientist-I in ICMR funded project (EMDR/IG/10-2023-0001022).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgger, E. M. (2016). Novel adjuvant formulations for delivery of anti-tuberculosis vaccine candidates. In \u003cem\u003eAdvanced Drug Delivery Reviews\u003c/em\u003e (Vol. 102, pp. 73\u0026ndash;82). Elsevier B.V. https://doi.org/10.1016/j.addr.2015.11.012\u003c/li\u003e\n\u003cli\u003eAl Tbeishat, H. (2022). Novel In Silico mRNA vaccine design exploiting proteins of M. tuberculosis that modulates host immune responses by inducing epigenetic modifications. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1). https://doi.org/10.1038/s41598-022-08506-4\u003c/li\u003e\n\u003cli\u003eBehzadi, P., Garc\u0026iacute;a-Perdomo, H. A., \u0026amp; Karpiński, T. M. (2021). Toll-Like Receptors: General Molecular and Structural Biology. In \u003cem\u003eJournal of Immunology Research\u003c/em\u003e (Vol. 2021). Hindawi Limited. https://doi.org/10.1155/2021/9914854\u003c/li\u003e\n\u003cli\u003eBellini, C., Vergara, E., Bencs, F., Fodor, K., Bősze, S., Krivić, D., Bacsa, B., Surguta, S. E., T\u0026oacute;v\u0026aacute;ri, J., Reljic, R., \u0026amp; Horv\u0026aacute;ti, K. (2023). 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Novel dual-pathogen multi-epitope mRNA vaccine development for Brucella melitensis and Mycobacterium tuberculosis in silico approach. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(10), e0309560. https://doi.org/10.1371/journal.pone.0309560\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Mycobacterium tuberculosis, immuno-informatics, mRNA vaccine, peptide-based vaccine candidates","lastPublishedDoi":"10.21203/rs.3.rs-6579802/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6579802/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tuberculosis (TB) is a disease which continues to challenge the global health system for decades. Despite several scientific interventions being carried out for its diagnosis and treatment, it is still the leading cause of mortality from any known infectious disease. Novel approaches for a promising TB vaccine necessitate identification of antigens that could confer protective immunity at all stages of infection and boosting immune response. mRNA vaccines are the vaccines of the future and offer a viable substitute for traditional vaccine techniques.\nEmploying an immuno-informatics approach, we designed mRNA vaccine with T cell- epitopes expressed through different stages of TB infection. In-silico results using IEDB and NETMHC4.0 shows strong affinity of designed vaccine for both class I MHC restricted CD8 T cells and class II MHC restricted CD4 T cells. Our designed mRNA vaccine based on selected epitopes along with extra co-translational mRNA structural component was predicted to be highly stable in RNA fold webserver. Codon optimization led to the optimal translation of the mRNA in the host cell. Molecular docking in Patchdock revealed strong interaction of designed construct for immune receptors- TLR2 and TLR4 which is further confirmed by MD simulations using Gromacs server. Both these receptors establish specific Leucine-Rich-Repeats (LRR) regions interactions with the vaccine construct, implicating their strong binding affinity for these immune receptors. C-Immsim based immune simulation studies substantiated translated protein immunogenic nature as a promising vaccine.\nOur approach of immunoinformatic based designing, synthesis and experimental validation of mRNA vaccine could be a promising strategy to combat TB.","manuscriptTitle":"Design and validation of multi-stage expressing mRNA vaccine for Mycobacterium tuberculosis through computational technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 05:27:36","doi":"10.21203/rs.3.rs-6579802/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":"f97cf259-886b-433d-b144-ddd4289df8d0","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T05:27:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 05:27:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6579802","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6579802","identity":"rs-6579802","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}