Multi-epitope vaccine construct against bovine tuberculosis: insights from immunoinformatics and molecular dynamics simulations

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Despite extensive control efforts, there is still no licensed vaccine available for effective prevention of bTB. This study aimed to design a multi-epitope vaccine candidate against Mycobacterium bovis using comprehensive immunoinformatics and molecular dynamics approaches. Eighteen high-confidence epitopes were predicted from antigenic bovine tuberculosis proteins through machine learning–based algorithms and subsequently assembled into a single vaccine construct using GPGPG linkers and an HSP70 adjuvant to enhance immunogenicity. The designed multi-epitope vaccine was predicted to be non-toxic and non-allergenic, as validated using CSM-Toxin and AllerTOP v2.0 servers. The tertiary structure of the vaccine was modeled and docked with Toll-like receptor 4 to assess molecular interactions and stability. Molecular dynamics simulations for 100 ns at 300 K revealed a stable vaccine–receptor complex with consistent hydrogen bonding and low RMSD fluctuations in a solvated environment. Overall, this study provides theoretical evidence supporting the rational design of a safe, stable, and potentially immunogenic multi-epitope vaccine candidate for bovine tuberculosis, warranting further in vitro and in vivo validation. Bovine tuberculosis Multi-epitope vaccine Molecular dynamics simulation Immunoinformatics Computational vaccinology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bovine tuberculosis (bTB) is one of the deadliest infectious diseases globally, causing an estimated 1.2 million deaths (range: 1.1–1.3 million) in 2019 alone. Of these deaths, approximately 11,400 (range: 4,470–21,600) were attributed to Mycobacterium bovis [1], a zoonotic pathogen that infects a variety of wild animals, including badgers, deer, hares, swine, and nonhuman primates[2, 3]. While the Bacille Calmette-Guérin (BCG) vaccine has been the only licensed vaccine for tuberculosis (TB) in humans since 1921[4], there is still no vaccine available for bTB. Although the BCG vaccine was reported to provide protection against bTB in cattle a decade before human trials[5], subsequent field studies showed variable efficacy, ranging from 0% to 80%[6]. Furthermore, the field use of BCG complicates infection control programs, as vaccinated animals may test positive in purified protein derivative (PPD) assays, which are used to differentiate infected from vaccinated animals (DIVA)[7]. Attempts have been made to use major antigenic targets of Mycobacterium bovis that are deleted in BCG as new DIVA candidates[8, 9]; however, the absence of these antigenic targets reduces the overall efficacy of TB vaccines[10, 11]. With advancements in computational resources, multi-epitope vaccines (MEV) present a promising solution to these challenges. MEV is an in silico process that uses the genetic information of a pathogen for vaccine discovery [12]. Compared to traditional live attenuated vaccines, subunit vaccines developed via MEV are faster to produce, more stable, and associated with fewer side effects[13]. This approach has been used to target numerous pathogens[14–17] including Mycobacterium Ulcerans [18], Mycobacterium Avium [19], and Mycobacterium tuberculosis [20], although further validation is still needed. The first successful application of MEV was a vaccine against invasive meningococcal disease [21], and recent MEV-based mRNA vaccines against COVID-19 have saved millions of lives[22]. As bTB continues to pose a serious threat to both public health and the livestock industry, resulting in considerable economic losses worldwide, the development of an effective vaccine remains a critical priority. Despite the global implementation of control measures and the availability of the Bacillus Calmette–Guérin (BCG) vaccine, its efficacy in cattle remains inconsistent, and it does not fully prevent infection or transmission. Therefore, novel vaccination strategies are urgently needed to complement existing control programs. In this study, we aimed to design a MEV candidate for bTB using in silico immunoinformatics approaches. The Mycobacterium bovis strain AF2122/97, which is frequently used as a reference genome in tuberculosis and bovine tuberculosis vaccine design studies [23–25], was employed as the source for epitope prediction and vaccine construction. 2. Materials and Methods 2.1. Protein sequence retrieval Epitope prediction and the analyses throughout the study were conducted on Outer membrane protein A (OmpA), 6 kDa early secretory antigenic target (Esat-6), and Esat-6 like protein EsxB (Cfp-10). Outer membrane protein A (OmpA) is a highly conserved and abundant virulence factor in Gram-negative bacteria[26]. Esat-6 and Cfp-10 are two of the major antigenic targets of the RD1 region, which is absent in BCG strains. RD1 region is responsible for the virulence in Mycobacterium bovis [27] and showed promising immunological responses in animal and clinical trials[28–32]. The protein sequences of bTB were obtained from Uniprot with the following entry identifiers: A0A679LDF2 (OmpA), P0A565 (Esat-6), P0A567 (Cfp-10), P0A5C0 (Hsp70). 2.2. Epitopes prediction The protein sequences were submitted to NetMHCpan v.4.1 ( https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/ ) and NetMHCIIpan v.4.3 ( https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.3/ ) to predict MHC-Ⅰ and MHC-Ⅱ epitopes respectively. All the bovine MHC alleles in each version were used, which is 105 alleles for BoLA in NetMHCpan v.4.1 and 365 alleles for BoLa-DRB3 in NetMHCIIpan v.4.3. Epitopes with peptide length of 9 were chosen for MHC-Ⅰ and 15mer was used for MHC-Ⅱ. Only the peptides with BindLevel of Weak binding (WB) and Strong binding (SB) were sorted by appearance counts in each MHC type (Supplementary Table S1 ). B cell epitopes were predicted by ABCpred ( https://webs.iiitd.edu.in/raghava/abcpred/ ) [33] with a default threshold of 0.51 and peptide length of 16. Non-overlapping peptides were kept for further analysis. 2.3. Construction of vaccine candidate sequence The top two non-overlapping epitope sequences were chosen by the appearance count for T cell epitopes and probability score for B cell epitopes. Epitopes were joined with the adjuvant protein HSP70 through the GPGPG linker. HSP70 is a highly conserved TLR4 agonist[34] that activates the innate immune system, especially the T helper cell response in TB and bTB [35–37]. 2.4. Allergenicity, antigenicity, toxicity, and physicochemical properties prediction of vaccine candidate As high allergenicity and toxicity can cause adverse health issues, the vaccine candidate sequence was tested with AllerTop 2.0 server ( https://www.ddg-pharmfac.net/AllerTOP/index.html ) and CSM-Toxin ( https://biosig.lab.uq.edu.au/csm_toxin/ ) respectively. To predict the antigenicity, VaxiJen v.2.0 ( http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html ) was used with a threshold of 0.4. To improve the efficacy of the vaccine candidate, solubility was evaluated with SOLpro ( https://scratch.proteomics.ics.uci.edu/ ) [38] and the physiochemical properties were estimated by the ProtParam server ( https://web.expasy.org/protparam/ ) [39]. 2.6. Vaccine’s three-dimensional structure prediction, refinement, and validation For the 3D structure prediction, AlphaFold2 [40] was used on the Neurosnap server ( https://neurosnap.ai/service/AlphaFold2?gclid=CjwKCAiA5L2tBhBTEiwAdSxJX1F8veoqgaIyza6QseaxhBJuD8pxeh3Xtdkkle9TjIi_-mOaZ9TkKhoCYN4QAvD_BwE ) with the default settings. AlphaFold2 showed remarkable results in the 14th critical Assessment of protein Structure prediction (CASP). The rank1 model was then refined with GalaxyRefine ( http://galaxy.seoklab.org/refine ) to increase the accuracy of the ab initio prediction method[41]. The validity of the final model was examined by ProSA-Web ( https://prosa.services.came.sbg.ac.at/prosa.php ) and Molprobity v4.4 ( https://swissmodel.expasy.org/assess ). 2.7. Molecular docking studies between the vaccine and immune receptor Toll-like receptor 4 (TLR4) 3D structure of Bos taurus from UniProt (Entry identifier: Q9GL65) was used as a docking target for the vaccine candidate. Toll-like receptor 4 is known to activate the host immune system by the recognition of our adjuvant HSP70 and other antigenic proteins of TB and bTB [36, 42–45]. Docking prediction was conducted with Cluspro ( https://cluspro.bu.edu/login.php ), which presents the energy-minimizing complex model by rotating the ligand against the receptor. The model with the highest number of cluster members for each option was considered the best-fitting model [46]. The interactions between the molecules after docking were shown with PDBsum ( https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/ ) [47]. 2.8. Molecular dynamics simulations of the vaccine-receptor complex Molecular dynamics (MD) simulation was conducted to investigate the stability of the vaccine-receptor complex using GROMACS 2023 software on a Linux operating system, which provides real-life environmental conditions for various biological models[48]. The OPLSAA force field and the SPC/E water model were employed to create of topology file. The MEV-TLR4 complex was then enclosed in a cubic box to remain intact, surrounded by 599,883 solvent molecules. Subsequently, to neutralize the charge, 43 Na + ions were added. The energy minimization was performed using the steepest descent algorithm with 50,000 steps, and the minimization process ceased when the maximum force reached < 1000.0 kJ/mol/nm. Following that, position restraints were applied during the equilibration process. NVT equilibration was executed at 300 K with 50,000 steps (100 ps), followed by NPT equilibration at 1 bar reference pressure with an additional 50,000 steps (100 ps). Afterward, production simulations for all-atom (1,826,988 atoms) were achieved using the NPT ensemble for 50,000,000 steps (100 ns). After the efficient completion of a 100 ns MD simulation, calculations were performed for the root mean square deviation (RMSD) of backbone residues, root mean square fluctuation (RMSF) of C-alpha, radius of gyration_total (Rg), and solvent accessible surface area (SASA). Additionally, to evaluate the stability of the complex as a whole during the simulation, we calculated the RMSD of the MEV-TLR4 complex at various time steps. By comparing the stability of the MEV-TLR4 complex, we were able to determine their respective stability. Afterward, the COCOMAPS tool[49] was used to conduct a thorough study and visualization of the contacts at the MEV-TLR4 complex interface. 3. Results 3.1. Epitope screening and vaccine candidate construction Total of 2,456 (OmpA: 1,462, Esat-6: 440, Cfp-10: 554) and 11,289 (OmpA: 8,357, Esat-6: 1,584, Cfp-10: 1,348) potential epitopes were predicted by NetMHCpan v.4.1 and NetMHCIIpan v.4.3 respectively. After sorting the epitopes by the appearance count, the top two non-overlapping epitopes were selected from each protein (Supplementary Data Sheets) for vaccine construction. From the total of 39 (OmpA: 24, Esat-6: 7, Cfp-10: 8) B cell epitopes sorted by the probability score of ABCpred, the top two non-overlapping epitopes were merged with the selected T cell epitopes, HSP70 adjuvant and GPGPG linkers (Fig. 1 ). Figure 1 The sequential overview of the vaccine construct. 3.2. Allergenicity, antigenicity, and toxicity prediction The merged sequence was tested for allergenicity with the k nearest neighbours ( k NN) based classifier AllerTop 2.0 server[50]. It was predicted to be a non-allergen with ARID1B (UniprotKB accession number: Q8NFD5) being the nearest protein. There was no sign of toxicity according to the Bidirectional Encoder Representations from Transformers (BERT) based CSM-Toxin[51]. Protective antigenicity was expected with a prediction value of 0.7838 using VaxiJen v2.0. Vaxijen classifies probable antigens by discriminant analysis using the partial least squares technique (DA-PLS)[52]. Considering these aspects as a safe antigen, our sequence complex was kept for further analysis. 3.3. Physiochemical properties and solubility prediction Physiochemical properties estimated by ProtParam gave insights into the vaccine candidate’s composition, stability, and hydropathicity (Table 1 ). The approximately 98kDA complex was estimated to have an instability index of 28.21, and the grand average of hydropathicity (GRAVY) index of -0.396. The probability of predicted solubility upon overexpression was 0.985522 according to SOLpro ( http://scratch.proteomics.ics.uci.edu/ ). Table 1 Physiochemical properties and overall traits of the vaccine construct. Properties Values Number of amino acids 955 Molecular weight 98573.67 Da Theoretical pI 4.91 Total number of negatively charged residues (Asp + Glu) 122 Total number of positively charged residues (Arg + Lys) 87 Formula C 4266 H 6872 N 1248 O 1411 S 11 Total number of atoms 13,808 Estimated half-life (mammalian reticulocytes, in vitro) 30 hours Estimated half-life (yeast, in vivo) > 20 hours Estimated half-life (Escherichia coli, in vivo) > 10 hours Instability index 28.21 (stable) Aliphatic index 79.30 Grand average of hydropathicity (GRAVY) -0.396 (hydrophilic) Solubility 0.985522 (probability) Allergenicity Non-allergen Antigenicity Probable antigen Toxicity Non-toxic 3.4. Validation of vaccine structure prediction The resulting models from AlphaFold2 prediction showed a mean predicted Local Distance Difference Test (pLDDT) score of 64.73 to 69.57 (Table S1 ). The Rank1 model with the highest pLDDT score was then refined with GalaxyRefine. Model 1 was the most improved model after refinement as stated by the lowest GDT-HA, RMSD, and less problematic values (Table S2 ). The validity of model 1 was inspected with ProSA-Web and Molprobity. Z-score value of the model was − 13.35, which is normal compared to other X-ray and NMR results (Fig. 2 a). The residues within the Ramachandran favoured region were expected to be 97.59% (Fig. 2 b). Figure 2 Validation of vaccine structure. (a) ProSA-Web validation; ( b) Molprobity Ramachandran plot. 3.5. Molecular docking of the vaccine and TLR4 receptor From the molecular docking results of Cluspro (Fig. 3 a), the model with the highest number of cluster members for the Van der Waals + electrostatics option was chosen regarding our input structures. Our selected model had 33 members in the cluster with a -268.4 weighted energy score calculated by the PIPER algorithm (Table S3). There were 13 hydrogen bonds and 10 salt bridges after docking (Fig. 3 b). The resulting model was used as an input for molecular dynamics simulation. Figure 3 Molecular docking of the vaccine and TLR4 receptor. (a) Docking complex; ( b) PDBsum summary of the residue interactions between the vaccine and TLR4 receptor. 3.6. Molecular dynamics simulation studies The RMSD of backbone residues over the course of a 100 ns trajectory was used to assess the lifetime of protein-protein complexes resulting in an average RMSD of 2.56 ± 0.62 nm (Fig. 4 a). Based on the RMSF analysis, residues of MEV showed considerable flexibility, with fluctuations of about 1.08 ± 0.44 nm, as shown in (Fig. 4 b). With an average of 6.90 ± 0.31 nm, the Rg analysis results, as shown in (Fig. 4 c), indicate that the MEV-TLR4 complex maintains a slight stability. Furthermore, (Fig. 4 d) shows the average SASA value of MEV-TLR4 complex was 949.37 ± 52.20 nm 2 . Figure 4 Molecular dynamics simulation studies of docking complex. (a) Root mean square deviation (RMSD); (b) Root mean square fluctuation (RMSF); (c) Radius of gyration (Rg); (d) Solvent-accessible surface area (SASA). Furthermore, by using intermolecular contact maps to find hot spot residues, COCOMAPS makes it possible to analyze and visualize the interface of interaction in protein complexes. While the results of RMSD and interaction map at various time steps show an interaction pattern with the particular contacts maintained (Figs. 5 a,b), it is clear that overall contacts remained steady throughout the simulation time. All the above analyses indicate the stability of interface interactions between the vaccine construct and TLR4. Figure 5 (a) Superimposition of the vaccine-TLR4 complex and it’s respective RMSD of different timelines; ( b) COCOMAPS contact map shows residue distances for each timeline. The dots in red, yellow, green and blue represent the residue distances below 7, 10, 13 and 16 angstrom. 4. Discussion Currently, there are no available vaccines for bTB due to challenges such as differentiating infected from vaccinated animals, as well as the low protective efficacy resulting from the lack of virulence regions in the tested strains. As such, designing an in silico-based MEV could be a cost-efficient approach, either as a substitute vaccine or to complement existing vaccines. In this study, we predicted the epitope regions of the major antigens ESAT-6, CFP-10, and OmpA from the Mycobacterium bovis strain AF2122/97 and combined them to create a stable vaccine candidate. Non-overlapping sequences predicted across multiple MHC alleles were used as epitopes to enhance vaccine coverage. Only epitopes with peptide lengths of 9 and 15, which are known to be optimal for MHCⅠ and MHCⅡ binding, respectively, were selected. Given the critical importance of vaccine safety, the vaccine candidate sequence was tested for toxicity and allergenicity, and it passed both assessments. The pLDDT score of the best model from AlphaFold2's 3D structure prediction of the vaccine was 69.57, which is slightly below the confidence threshold of 70. Since side-chain structures are challenging to predict using ab initio methods, side-chain refinement was performed to enhance the accuracy of the vaccine's 3D structure. The refined structure exhibited improved side-chain folding and fell well within normal parameters when evaluated using ProSA-Web and Molprobity. To assess the dynamic stability of the protein-protein binding complex, a 100 ns molecular dynamics (MD) simulation was conducted. The main goal was to measure the difference between the protein’s backbone conformation at the start and end of the simulation. The dynamical stability of a biological molecule can be inferred from the observed variations over the simulation period. A stable complex is typically indicated by minimal divergence between the initial and final states. Additionally, RMSFs of C-alpha were calculated to provide insights into the dynamics-function relationship, derived from the divergence of protein motions during evolution. Residual flexibility or rigidity is crucial for biological processes such as molecular recognition, binding/unbinding, and catalysis. A region with a high RMSF score is considered more flexible, whereas regions with lower RMSF scores show minimal movement during the experiment. According to the RMSD and RMSF results, residues in the TLR4 portion exhibited minor fluctuations during the simulation, but they quickly stabilized. Following this, the radius of gyration (Rg) was calculated as a simulation-based metric for structural compactness. Rg, which represents the root mean square distance between the center of mass of each atom cluster, was plotted against time to show the degree of protein compactness during the MD simulation. Variable Rg values indicate protein unfolding or folding, while constant values suggest no significant change in protein folding. Solvent-accessible surface area (SASA) values gradually decreased throughout the simulation, indicating that the complexes became more compact or less solvent-exposed. Significant structural rearrangement likely occurred within the first 40 ns of the simulation, as evidenced by the rapid decrease in SASA during this period. After this, the structure remained stable for the remainder of the simulation. 5. Conclusion This study employed an immunoinformatics-driven pipeline to design a safe, stable, and potentially immunogenic MEV candidate against bTB. Eighteen highly antigenic epitopes were selected from Mycobacterium bovis proteins and assembled into a single construct incorporating a bTB-specific HSP70 adjuvant and GPGPG linkers to enhance both T-cell and B-cell responses. The designed MEV was predicted to be non-toxic, non-allergenic, and physiochemically stable based on multiple bioinformatics evaluations. Molecular docking and molecular dynamics simulations further confirmed a strong and stable interaction between the vaccine construct and the TLR4, indicating its potential to elicit robust immune activation. Overall, the findings provide theoretical evidence supporting the rational design of an effective MEV candidate against bTB. However, comprehensive in vitro and in vivo validations remain essential to confirm its immunogenicity and protective efficacy before clinical or veterinary application. Declarations Acknowledgements The authors are thankful to the laboratory of Bioinformatics and Population genetics (BIOPOP), Seoul National University, for providing the computing infrastructure to implement and execute the research work. Funding No funding was received to assist with the preparation of this manuscript. CRediT authorship contribution statement T.L.N : Conceptualization, Methodology, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review & editing, Supervision. All authors have read and agreed to the published version of the manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability All data generated and analyzed during this study are included in this article. References Global tuberculosis report 2020. (n.d.). Retrieved 8 January 2024, from https://www.who.int/publications/i/item/9789240013131 LoBue, P. A., Enarson, D. A., & Thoen, C. O. (n.d.). Tuberculosis in humans and animals: an overview [Serialised article. Tuberculosis: a re-emerging disease in animals and humans. Number 1 in the series]. Buddle, B. M., Skinner, M. A., & Chambers, M. A. (2000). 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Characterization of the functional properties of the 70-kDa protein of Mycobacterium bovis. Journal of Biological Chemistry , 266 (31), 20828–20832. https://doi.org/10.1016/S0021-9258(18)54784-0 Bulut, Y., Michelsen, K. S., Hayrapetian, L., Naiki, Y., Spallek, R., Singh, M., & Arditi, M. (2005). Mycobacterium Tuberculosis Heat Shock Proteins Use Diverse Toll-like Receptor Pathways to Activate Pro-inflammatory Signals. Journal of Biological Chemistry , 280 (22), 20961–20967. https://doi.org/10.1074/JBC.M411379200 Fang, H., Wu, Y., Huang, X., Wang, W., Ang, B., Cao, X., & Wan, T. (2011). Toll-like Receptor 4 (TLR4) Is Essential for Hsp70-like Protein 1 (HSP70L1) to Activate Dendritic Cells and Induce Th1 Response. The Journal of Biological Chemistry , 286 (35), 30393. https://doi.org/10.1074/JBC.M111.266528 Magnan, C. N., Randall, A., & Baldi, P. (2009). SOLpro: accurate sequence-based prediction of protein solubility. 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Control of Mycobacterium bovis BCG infection with increased inflammation in TLR4-deficient mice. Microbes and Infection , 5 (12), 1070–1081. https://doi.org/10.1016/J.MICINF.2003.06.001 Vabulas, R. M., Ahmad-Nejad, P., Ghose, S., Kirschning, C. J., Issels, R. D., & Wagner, H. (2002). HSP70 as Endogenous Stimulus of the Toll/Interleukin-1 Receptor Signal Pathway. Journal of Biological Chemistry , 277 (17), 15107–15112. https://doi.org/10.1074/JBC.M111204200 Asea, A., Rehli, M., Kabingu, E., Boch, J. A., Baré, O., Auron, P. E., … Calderwood, S. K. (2002). Novel Signal Transduction Pathway Utilized by Extracellular HSP70 ROLE OF Toll-LIKE RECEPTOR (TLR) 2 AND TLR4*. Journal of Biological Chemistry , 277 , 15028–15034. https://doi.org/10.1074/jbc.M200497200 Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., … Vajda, S. (2017). The ClusPro web server for protein-protein docking. Nature protocols , 12 (2), 255. https://doi.org/10.1038/NPROT.2016.169 Laskowski, R. A., Jablonska, J., Pravda, L., Vareková, R. S., & Thornton, J. M. (2018). PDBsum: Structural summaries of PDB entries. Protein Science : A Publication of the Protein Society , 27 (1), 129. https://doi.org/10.1002/PRO.3289 Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindah, E. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX , 1–2 , 19–25. https://doi.org/10.1016/J.SOFTX.2015.06.001 Vangone, A., Spinelli, R., Scarano, V., Cavallo, L., & Oliva, R. (2011). COCOMAPS: a web application to analyze and visualize contacts at the interface of biomolecular complexes. Bioinformatics , 27 (20), 2915–2916. https://doi.org/10.1093/BIOINFORMATICS/BTR484 Dimitrov, I., Bangov, I., Flower, D. R., & Doytchinova, I. (2014). AllerTOP v.2 - A server for in silico prediction of allergens. Journal of Molecular Modeling , 20 (6), 1–6. https://doi.org/10.1007/S00894-014-2278-5/TABLES/3 Morozov, V., Rodrigues, C. H. M., & Ascher, D. B. (2023). CSM-Toxin: A Web-Server for Predicting Protein Toxicity. Pharmaceutics , 15 (2), 431. https://doi.org/10.3390/PHARMACEUTICS15020431/S1 Doytchinova, I. A., & Flower, D. R. (2007). VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics , 8 (1), 1–7. https://doi.org/10.1186/1471-2105-8-4/TABLES/2 Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataSheets.xlsx SupplementaryInformation.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. 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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-8331959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558831571,"identity":"9eb2a852-2166-4e9c-906b-5378da2f859d","order_by":0,"name":"Truc Ly 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16:55:05","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89133,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/49f572ed732a6015553acb04.html"},{"id":98282021,"identity":"1a20bdc6-4ca6-4b91-a5dc-9c884779931a","added_by":"auto","created_at":"2025-12-16 05:50:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89642,"visible":true,"origin":"","legend":"\u003cp\u003eThe sequential overview of the vaccine construct.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/369902d8630961b3e57aaec7.png"},{"id":98282015,"identity":"323f5d68-d6fd-4101-93c3-64f2baa56d4f","added_by":"auto","created_at":"2025-12-16 05:50:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":763444,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of vaccine structure. \u003cstrong\u003e(a)\u003c/strong\u003eProSA-Web validation; (\u003cstrong\u003eb)\u003c/strong\u003e Molprobity Ramachandran plot.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/ad94540808f549460135d3ce.png"},{"id":98282017,"identity":"7ae5c2b0-a612-4570-a42c-45d57da4c816","added_by":"auto","created_at":"2025-12-16 05:50:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":442904,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of the vaccine and TLR4 receptor. \u003cstrong\u003e(a)\u003c/strong\u003e Docking complex; (\u003cstrong\u003eb)\u003c/strong\u003e PDBsum summary of the residue interactions between the vaccine and TLR4 receptor.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/ddd1613a396158cde64d994f.png"},{"id":98435373,"identity":"04a9188f-8786-4ce1-ad14-cdb6f1245e7c","added_by":"auto","created_at":"2025-12-17 16:53:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":422383,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular dynamics simulation studies of docking complex. (a) Root mean square deviation (RMSD); (b) Root mean square fluctuation (RMSF); (c) Radius of gyration (Rg); (d) Solvent-accessible surface area (SASA).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/a265c8e366d5ac195830eb2c.png"},{"id":98435304,"identity":"fcaa4919-5052-4058-9876-6e19086956c8","added_by":"auto","created_at":"2025-12-17 16:53:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":987995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eSuperimposition of the vaccine-TLR4 complex and it’s respective RMSD of different timelines; (\u003cstrong\u003eb)\u003c/strong\u003eCOCOMAPS contact map shows residue distances for each timeline. The dots in red, yellow, green and blue represent the residue distances below 7, 10, 13 and 16 angstrom.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/7c08bb50458c4dacb4d542af.png"},{"id":100414546,"identity":"5c5e8ecc-bfa4-4879-893b-d3cf7e9ecd12","added_by":"auto","created_at":"2026-01-16 13:19:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3506232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/a84a2402-56f2-41d9-923d-268c46eea55d.pdf"},{"id":98435881,"identity":"21d73bf9-8f03-440d-ba5b-6c010f6430cc","added_by":"auto","created_at":"2025-12-17 16:54:32","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1182972,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataSheets.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/b3297ed290dafc4b3a9d6b54.xlsx"},{"id":98282013,"identity":"59cd34db-427b-4807-a39e-b685fec78ac2","added_by":"auto","created_at":"2025-12-16 05:50:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22767,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8331959/v1/5be7ae1a755b62d18035e678.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-epitope vaccine construct against bovine tuberculosis: insights from immunoinformatics and molecular dynamics simulations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBovine tuberculosis (bTB) is one of the deadliest infectious diseases globally, causing an estimated 1.2\u0026nbsp;million deaths (range: 1.1\u0026ndash;1.3\u0026nbsp;million) in 2019 alone. Of these deaths, approximately 11,400 (range: 4,470\u0026ndash;21,600) were attributed to \u003cem\u003eMycobacterium bovis\u003c/em\u003e [1], a zoonotic pathogen that infects a variety of wild animals, including badgers, deer, hares, swine, and nonhuman primates[2, 3]. While the Bacille Calmette-Gu\u0026eacute;rin (BCG) vaccine has been the only licensed vaccine for tuberculosis (TB) in humans since 1921[4], there is still no vaccine available for bTB. Although the BCG vaccine was reported to provide protection against bTB in cattle a decade before human trials[5], subsequent field studies showed variable efficacy, ranging from 0% to 80%[6]. Furthermore, the field use of BCG complicates infection control programs, as vaccinated animals may test positive in purified protein derivative (PPD) assays, which are used to differentiate infected from vaccinated animals (DIVA)[7]. Attempts have been made to use major antigenic targets of \u003cem\u003eMycobacterium bovis\u003c/em\u003e that are deleted in BCG as new DIVA candidates[8, 9]; however, the absence of these antigenic targets reduces the overall efficacy of TB vaccines[10, 11].\u003c/p\u003e \u003cp\u003eWith advancements in computational resources, multi-epitope vaccines (MEV) present a promising solution to these challenges. MEV is an in silico process that uses the genetic information of a pathogen for vaccine discovery [12]. Compared to traditional live attenuated vaccines, subunit vaccines developed via MEV are faster to produce, more stable, and associated with fewer side effects[13]. This approach has been used to target numerous pathogens[14\u0026ndash;17] including \u003cem\u003eMycobacterium Ulcerans\u003c/em\u003e[18], \u003cem\u003eMycobacterium Avium\u003c/em\u003e[19], and \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e[20], although further validation is still needed. The first successful application of MEV was a vaccine against invasive meningococcal disease [21], and recent MEV-based mRNA vaccines against COVID-19 have saved millions of lives[22].\u003c/p\u003e \u003cp\u003eAs bTB continues to pose a serious threat to both public health and the livestock industry, resulting in considerable economic losses worldwide, the development of an effective vaccine remains a critical priority. Despite the global implementation of control measures and the availability of the Bacillus Calmette\u0026ndash;Gu\u0026eacute;rin (BCG) vaccine, its efficacy in cattle remains inconsistent, and it does not fully prevent infection or transmission. Therefore, novel vaccination strategies are urgently needed to complement existing control programs. In this study, we aimed to design a MEV candidate for bTB using in silico immunoinformatics approaches. The \u003cem\u003eMycobacterium bovis\u003c/em\u003e strain AF2122/97, which is frequently used as a reference genome in tuberculosis and bovine tuberculosis vaccine design studies [23\u0026ndash;25], was employed as the source for epitope prediction and vaccine construction.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Protein sequence retrieval\u003c/h2\u003e \u003cp\u003eEpitope prediction and the analyses throughout the study were conducted on Outer membrane protein A (OmpA), 6 kDa early secretory antigenic target (Esat-6), and Esat-6 like protein EsxB (Cfp-10). Outer membrane protein A (OmpA) is a highly conserved and abundant virulence factor in Gram-negative bacteria[26]. Esat-6 and Cfp-10 are two of the major antigenic targets of the RD1 region, which is absent in BCG strains. RD1 region is responsible for the virulence in \u003cem\u003eMycobacterium bovis\u003c/em\u003e[27] and showed promising immunological responses in animal and clinical trials[28\u0026ndash;32]. The protein sequences of bTB were obtained from Uniprot with the following entry identifiers: A0A679LDF2 (OmpA), P0A565 (Esat-6), P0A567 (Cfp-10), P0A5C0 (Hsp70).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Epitopes prediction\u003c/h2\u003e \u003cp\u003eThe protein sequences were submitted to NetMHCpan v.4.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/NetMHCpan-4.1/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/NetMHCpan-4.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and NetMHCIIpan v.4.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://services.healthtech.dtu.dk/services/NetMHCIIpan-4.3/\u003c/span\u003e\u003cspan address=\"https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to predict MHC-Ⅰ and MHC-Ⅱ epitopes respectively. All the bovine MHC alleles in each version were used, which is 105 alleles for BoLA in NetMHCpan v.4.1 and 365 alleles for BoLa-DRB3 in NetMHCIIpan v.4.3. Epitopes with peptide length of 9 were chosen for MHC-Ⅰ and 15mer was used for MHC-Ⅱ. Only the peptides with BindLevel of Weak binding (WB) and Strong binding (SB) were sorted by appearance counts in each MHC type (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). B cell epitopes were predicted by ABCpred (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webs.iiitd.edu.in/raghava/abcpred/\u003c/span\u003e\u003cspan address=\"https://webs.iiitd.edu.in/raghava/abcpred/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [33] with a default threshold of 0.51 and peptide length of 16. Non-overlapping peptides were kept for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Construction of vaccine candidate sequence\u003c/h2\u003e \u003cp\u003eThe top two non-overlapping epitope sequences were chosen by the appearance count for T cell epitopes and probability score for B cell epitopes. Epitopes were joined with the adjuvant protein HSP70 through the GPGPG linker. HSP70 is a highly conserved TLR4 agonist[34] that activates the innate immune system, especially the T helper cell response in TB and bTB [35\u0026ndash;37].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Allergenicity, antigenicity, toxicity, and physicochemical properties prediction of vaccine candidate\u003c/h2\u003e \u003cp\u003eAs high allergenicity and toxicity can cause adverse health issues, the vaccine candidate sequence was tested with AllerTop 2.0 server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ddg-pharmfac.net/AllerTOP/index.html\u003c/span\u003e\u003cspan address=\"https://www.ddg-pharmfac.net/AllerTOP/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CSM-Toxin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/csm_toxin/\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/csm_toxin/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) respectively. To predict the antigenicity, VaxiJen v.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\u003c/span\u003e\u003cspan address=\"http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used with a threshold of 0.4.\u003c/p\u003e \u003cp\u003eTo improve the efficacy of the vaccine candidate, solubility was evaluated with SOLpro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scratch.proteomics.ics.uci.edu/\u003c/span\u003e\u003cspan address=\"https://scratch.proteomics.ics.uci.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [38] and the physiochemical properties were estimated by the ProtParam server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://web.expasy.org/protparam/\u003c/span\u003e\u003cspan address=\"https://web.expasy.org/protparam/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [39].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Vaccine\u0026rsquo;s three-dimensional structure prediction, refinement, and validation\u003c/h2\u003e \u003cp\u003eFor the 3D structure prediction, AlphaFold2 [40] was used on the Neurosnap server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://neurosnap.ai/service/AlphaFold2?gclid=CjwKCAiA5L2tBhBTEiwAdSxJX1F8veoqgaIyza6QseaxhBJuD8pxeh3Xtdkkle9TjIi_-mOaZ9TkKhoCYN4QAvD_BwE\u003c/span\u003e\u003cspan address=\"https://neurosnap.ai/service/AlphaFold2?gclid=CjwKCAiA5L2tBhBTEiwAdSxJX1F8veoqgaIyza6QseaxhBJuD8pxeh3Xtdkkle9TjIi_-mOaZ9TkKhoCYN4QAvD_BwE\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with the default settings. AlphaFold2 showed remarkable results in the 14th critical Assessment of protein Structure prediction (CASP). The rank1 model was then refined with GalaxyRefine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://galaxy.seoklab.org/refine\u003c/span\u003e\u003cspan address=\"http://galaxy.seoklab.org/refine\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to increase the accuracy of the \u003cem\u003eab initio\u003c/em\u003e prediction method[41]. The validity of the final model was examined by ProSA-Web (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prosa.services.came.sbg.ac.at/prosa.php\u003c/span\u003e\u003cspan address=\"https://prosa.services.came.sbg.ac.at/prosa.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Molprobity v4.4 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://swissmodel.expasy.org/assess\u003c/span\u003e\u003cspan address=\"https://swissmodel.expasy.org/assess\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Molecular docking studies between the vaccine and immune receptor\u003c/h2\u003e \u003cp\u003eToll-like receptor 4 (TLR4) 3D structure of Bos taurus from UniProt (Entry identifier: Q9GL65) was used as a docking target for the vaccine candidate. Toll-like receptor 4 is known to activate the host immune system by the recognition of our adjuvant HSP70 and other antigenic proteins of TB and bTB [36, 42\u0026ndash;45]. Docking prediction was conducted with Cluspro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cluspro.bu.edu/login.php\u003c/span\u003e\u003cspan address=\"https://cluspro.bu.edu/login.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which presents the energy-minimizing complex model by rotating the ligand against the receptor. The model with the highest number of cluster members for each option was considered the best-fitting model [46]. The interactions between the molecules after docking were shown with PDBsum (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [47].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Molecular dynamics simulations of the vaccine-receptor complex\u003c/h2\u003e \u003cp\u003eMolecular dynamics (MD) simulation was conducted to investigate the stability of the vaccine-receptor complex using GROMACS 2023 software on a Linux operating system, which provides real-life environmental conditions for various biological models[48]. The OPLSAA force field and the SPC/E water model were employed to create of topology file. The MEV-TLR4 complex was then enclosed in a cubic box to remain intact, surrounded by 599,883 solvent molecules. Subsequently, to neutralize the charge, 43 Na\u003csup\u003e+\u003c/sup\u003e ions were added. The energy minimization was performed using the steepest descent algorithm with 50,000 steps, and the minimization process ceased when the maximum force reached\u0026thinsp;\u0026lt;\u0026thinsp;1000.0 kJ/mol/nm. Following that, position restraints were applied during the equilibration process. NVT equilibration was executed at 300 K with 50,000 steps (100 ps), followed by NPT equilibration at 1 bar reference pressure with an additional 50,000 steps (100 ps). Afterward, production simulations for all-atom (1,826,988 atoms) were achieved using the NPT ensemble for 50,000,000 steps (100 ns). After the efficient completion of a 100 ns MD simulation, calculations were performed for the root mean square deviation (RMSD) of backbone residues, root mean square fluctuation (RMSF) of C-alpha, radius of gyration_total (Rg), and solvent accessible surface area (SASA).\u003c/p\u003e \u003cp\u003eAdditionally, to evaluate the stability of the complex as a whole during the simulation, we calculated the RMSD of the MEV-TLR4 complex at various time steps. By comparing the stability of the MEV-TLR4 complex, we were able to determine their respective stability. Afterward, the COCOMAPS tool[49] was used to conduct a thorough study and visualization of the contacts at the MEV-TLR4 complex interface.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Epitope screening and vaccine candidate construction\u003c/h2\u003e \u003cp\u003eTotal of 2,456 (OmpA: 1,462, Esat-6: 440, Cfp-10: 554) and 11,289 (OmpA: 8,357, Esat-6: 1,584, Cfp-10: 1,348) potential epitopes were predicted by NetMHCpan v.4.1 and NetMHCIIpan v.4.3 respectively. After sorting the epitopes by the appearance count, the top two non-overlapping epitopes were selected from each protein (Supplementary Data Sheets) for vaccine construction. From the total of 39 (OmpA: 24, Esat-6: 7, Cfp-10: 8) B cell epitopes sorted by the probability score of ABCpred, the top two non-overlapping epitopes were merged with the selected T cell epitopes, HSP70 adjuvant and GPGPG linkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e The sequential overview of the vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Allergenicity, antigenicity, and toxicity prediction\u003c/h2\u003e \u003cp\u003eThe merged sequence was tested for allergenicity with the \u003cem\u003ek\u003c/em\u003e nearest neighbours (\u003cem\u003ek\u003c/em\u003eNN) based classifier AllerTop 2.0 server[50]. It was predicted to be a non-allergen with ARID1B (UniprotKB accession number: Q8NFD5) being the nearest protein. There was no sign of toxicity according to the Bidirectional Encoder Representations from Transformers (BERT) based CSM-Toxin[51]. Protective antigenicity was expected with a prediction value of 0.7838 using VaxiJen v2.0. Vaxijen classifies probable antigens by discriminant analysis using the partial least squares technique (DA-PLS)[52]. Considering these aspects as a safe antigen, our sequence complex was kept for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Physiochemical properties and solubility prediction\u003c/h2\u003e \u003cp\u003ePhysiochemical properties estimated by ProtParam gave insights into the vaccine candidate\u0026rsquo;s composition, stability, and hydropathicity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The approximately 98kDA complex was estimated to have an instability index of 28.21, and the grand average of hydropathicity (GRAVY) index of -0.396. The probability of predicted solubility upon overexpression was 0.985522 according to SOLpro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://scratch.proteomics.ics.uci.edu/\u003c/span\u003e\u003cspan address=\"http://scratch.proteomics.ics.uci.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\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\u003ePhysiochemical properties and overall traits of the vaccine construct.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProperties\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of amino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecular weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98573.67 Da\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical pI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of negatively charged residues (Asp\u0026thinsp;+\u0026thinsp;Glu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of positively charged residues (Arg\u0026thinsp;+\u0026thinsp;Lys)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e4266\u003c/sub\u003eH\u003csub\u003e6872\u003c/sub\u003eN\u003csub\u003e1248\u003c/sub\u003eO\u003csub\u003e1411\u003c/sub\u003eS\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of atoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated half-life (mammalian reticulocytes, in vitro)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated half-life (yeast, in vivo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimated half-life (Escherichia coli, in vivo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 hours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstability index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.21 (stable)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAliphatic index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrand average of hydropathicity (GRAVY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.396 (hydrophilic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolubility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.985522 (probability)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllergenicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-allergen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntigenicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProbable antigen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-toxic\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Validation of vaccine structure prediction\u003c/h2\u003e \u003cp\u003eThe resulting models from AlphaFold2 prediction showed a mean predicted Local Distance Difference Test (pLDDT) score of 64.73 to 69.57 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The Rank1 model with the highest pLDDT score was then refined with GalaxyRefine. Model 1 was the most improved model after refinement as stated by the lowest GDT-HA, RMSD, and less problematic values (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The validity of model 1 was inspected with ProSA-Web and Molprobity. Z-score value of the model was \u0026minus;\u0026thinsp;13.35, which is normal compared to other X-ray and NMR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The residues within the Ramachandran favoured region were expected to be 97.59% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Validation of vaccine structure. \u003cb\u003e(a)\u003c/b\u003e ProSA-Web validation; (\u003cb\u003eb)\u003c/b\u003e Molprobity Ramachandran plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Molecular docking of the vaccine and TLR4 receptor\u003c/h2\u003e \u003cp\u003eFrom the molecular docking results of Cluspro (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the model with the highest number of cluster members for the Van der Waals\u0026thinsp;+\u0026thinsp;electrostatics option was chosen regarding our input structures. Our selected model had 33 members in the cluster with a -268.4 weighted energy score calculated by the PIPER algorithm (Table S3). There were 13 hydrogen bonds and 10 salt bridges after docking (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The resulting model was used as an input for molecular dynamics simulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Molecular docking of the vaccine and TLR4 receptor. \u003cb\u003e(a)\u003c/b\u003e Docking complex; (\u003cb\u003eb)\u003c/b\u003e PDBsum summary of the residue interactions between the vaccine and TLR4 receptor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Molecular dynamics simulation studies\u003c/h2\u003e \u003cp\u003eThe RMSD of backbone residues over the course of a 100 ns trajectory was used to assess the lifetime of protein-protein complexes resulting in an average RMSD of 2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62 nm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Based on the RMSF analysis, residues of MEV showed considerable flexibility, with fluctuations of about 1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 nm, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). With an average of 6.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 nm, the Rg analysis results, as shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), indicate that the MEV-TLR4 complex maintains a slight stability. Furthermore, (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed) shows the average SASA value of MEV-TLR4 complex was 949.37\u0026thinsp;\u0026plusmn;\u0026thinsp;52.20 nm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Molecular dynamics simulation studies of docking complex. \u003cb\u003e(a)\u003c/b\u003e Root mean square deviation (RMSD); \u003cb\u003e(b)\u003c/b\u003e Root mean square fluctuation (RMSF); \u003cb\u003e(c)\u003c/b\u003e Radius of gyration (Rg); \u003cb\u003e(d)\u003c/b\u003e Solvent-accessible surface area (SASA).\u003c/p\u003e \u003cp\u003eFurthermore, by using intermolecular contact maps to find hot spot residues, COCOMAPS makes it possible to analyze and visualize the interface of interaction in protein complexes. While the results of RMSD and interaction map at various time steps show an interaction pattern with the particular contacts maintained (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b), it is clear that overall contacts remained steady throughout the simulation time. All the above analyses indicate the stability of interface interactions between the vaccine construct and TLR4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e(a)\u003c/b\u003e Superimposition of the vaccine-TLR4 complex and it\u0026rsquo;s respective RMSD of different timelines; (\u003cb\u003eb)\u003c/b\u003e COCOMAPS contact map shows residue distances for each timeline. The dots in red, yellow, green and blue represent the residue distances below 7, 10, 13 and 16 angstrom.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCurrently, there are no available vaccines for bTB due to challenges such as differentiating infected from vaccinated animals, as well as the low protective efficacy resulting from the lack of virulence regions in the tested strains. As such, designing an in silico-based MEV could be a cost-efficient approach, either as a substitute vaccine or to complement existing vaccines.\u003c/p\u003e \u003cp\u003eIn this study, we predicted the epitope regions of the major antigens ESAT-6, CFP-10, and OmpA from the Mycobacterium bovis strain AF2122/97 and combined them to create a stable vaccine candidate. Non-overlapping sequences predicted across multiple MHC alleles were used as epitopes to enhance vaccine coverage. Only epitopes with peptide lengths of 9 and 15, which are known to be optimal for MHCⅠ and MHCⅡ binding, respectively, were selected. Given the critical importance of vaccine safety, the vaccine candidate sequence was tested for toxicity and allergenicity, and it passed both assessments.\u003c/p\u003e \u003cp\u003eThe pLDDT score of the best model from AlphaFold2's 3D structure prediction of the vaccine was 69.57, which is slightly below the confidence threshold of 70. Since side-chain structures are challenging to predict using ab initio methods, side-chain refinement was performed to enhance the accuracy of the vaccine's 3D structure. The refined structure exhibited improved side-chain folding and fell well within normal parameters when evaluated using ProSA-Web and Molprobity.\u003c/p\u003e \u003cp\u003eTo assess the dynamic stability of the protein-protein binding complex, a 100 ns molecular dynamics (MD) simulation was conducted. The main goal was to measure the difference between the protein\u0026rsquo;s backbone conformation at the start and end of the simulation. The dynamical stability of a biological molecule can be inferred from the observed variations over the simulation period. A stable complex is typically indicated by minimal divergence between the initial and final states. Additionally, RMSFs of C-alpha were calculated to provide insights into the dynamics-function relationship, derived from the divergence of protein motions during evolution. Residual flexibility or rigidity is crucial for biological processes such as molecular recognition, binding/unbinding, and catalysis. A region with a high RMSF score is considered more flexible, whereas regions with lower RMSF scores show minimal movement during the experiment.\u003c/p\u003e \u003cp\u003eAccording to the RMSD and RMSF results, residues in the TLR4 portion exhibited minor fluctuations during the simulation, but they quickly stabilized. Following this, the radius of gyration (Rg) was calculated as a simulation-based metric for structural compactness. Rg, which represents the root mean square distance between the center of mass of each atom cluster, was plotted against time to show the degree of protein compactness during the MD simulation. Variable Rg values indicate protein unfolding or folding, while constant values suggest no significant change in protein folding. Solvent-accessible surface area (SASA) values gradually decreased throughout the simulation, indicating that the complexes became more compact or less solvent-exposed. Significant structural rearrangement likely occurred within the first 40 ns of the simulation, as evidenced by the rapid decrease in SASA during this period. After this, the structure remained stable for the remainder of the simulation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study employed an immunoinformatics-driven pipeline to design a safe, stable, and potentially immunogenic MEV candidate against bTB. Eighteen highly antigenic epitopes were selected from \u003cem\u003eMycobacterium bovis\u003c/em\u003e proteins and assembled into a single construct incorporating a bTB-specific HSP70 adjuvant and GPGPG linkers to enhance both T-cell and B-cell responses. The designed MEV was predicted to be non-toxic, non-allergenic, and physiochemically stable based on multiple bioinformatics evaluations. Molecular docking and molecular dynamics simulations further confirmed a strong and stable interaction between the vaccine construct and the TLR4, indicating its potential to elicit robust immune activation. Overall, the findings provide theoretical evidence supporting the rational design of an effective MEV candidate against bTB. However, comprehensive in vitro and in vivo validations remain essential to confirm its immunogenicity and protective efficacy before clinical or veterinary application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the laboratory of Bioinformatics and Population genetics (BIOPOP), Seoul National University, for providing the computing infrastructure to implement and execute the research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT.L.N\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Supervision. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated and analyzed during this study are included in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal tuberculosis report 2020. (n.d.). Retrieved 8 January 2024, from https://www.who.int/publications/i/item/9789240013131\u003c/li\u003e\n\u003cli\u003eLoBue, P. A., Enarson, D. A., \u0026amp; Thoen, C. O. (n.d.). Tuberculosis in humans and animals: an overview [Serialised article. Tuberculosis: a re-emerging disease in animals and humans. 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VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 1\u0026ndash;7. https://doi.org/10.1186/1471-2105-8-4/TABLES/2\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":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":"Bovine tuberculosis, Multi-epitope vaccine, Molecular dynamics simulation, Immunoinformatics, Computational vaccinology","lastPublishedDoi":"10.21203/rs.3.rs-8331959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8331959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBovine tuberculosis is a chronic zoonotic disease that continues to threaten the livestock industry worldwide, particularly in developing countries. Despite extensive control efforts, there is still no licensed vaccine available for effective prevention of bTB. This study aimed to design a multi-epitope vaccine candidate against \u003cem\u003eMycobacterium bovis\u003c/em\u003e using comprehensive immunoinformatics and molecular dynamics approaches. Eighteen high-confidence epitopes were predicted from antigenic bovine tuberculosis proteins through machine learning\u0026ndash;based algorithms and subsequently assembled into a single vaccine construct using GPGPG linkers and an HSP70 adjuvant to enhance immunogenicity. The designed multi-epitope vaccine was predicted to be non-toxic and non-allergenic, as validated using CSM-Toxin and AllerTOP v2.0 servers. The tertiary structure of the vaccine was modeled and docked with Toll-like receptor 4 to assess molecular interactions and stability. Molecular dynamics simulations for 100 ns at 300 K revealed a stable vaccine\u0026ndash;receptor complex with consistent hydrogen bonding and low RMSD fluctuations in a solvated environment. Overall, this study provides theoretical evidence supporting the rational design of a safe, stable, and potentially immunogenic multi-epitope vaccine candidate for bovine tuberculosis, warranting further in vitro and in vivo validation.\u003c/p\u003e","manuscriptTitle":"Multi-epitope vaccine construct against bovine tuberculosis: insights from immunoinformatics and molecular dynamics simulations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 05:50:32","doi":"10.21203/rs.3.rs-8331959/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":"1ffe09ab-d348-4333-8a30-de80d0aeccc1","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T11:40:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 05:50:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8331959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8331959","identity":"rs-8331959","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}