Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Designing a novel multiepitope vaccine candidate against Treponema pallidum via adhesins using reverse vaccinology
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 01 April 2026

Designing a novel multiepitope vaccine candidate against Treponema pallidum via adhesins using reverse vaccinology

  • Hongmei Tang1,5 na1,
  • Zhixi Chen1,3 na1,
  • Hongxia Yan4,
  • Zhen He5,
  • Ranhui Li5,
  • Yafeng Xie2 &
  • …
  • Xiaoliu Wang1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biotechnology
  • Computational biology and bioinformatics
  • Immunology
  • Microbiology

Abstract

Syphilis, caused by Treponema pallidum (T. pallidum), represents a significant worldwide public health threat. This spiral-shaped, Gram-negative pathogen is a strict human-specific obligate parasite primarily transmitted through sexual contact. This pathogen induces a multistage and multisystem progressive disease, against which no effective prophylactic vaccine currently exists. This study focuses on syphilis prevention and control by employing a reverse vaccinology approach to investigate the immunogenic properties of T. pallidum adhesin proteins. Fifteen T-cell epitopes and seven B-cell epitopes were screened and linked in series using appropriate linkers to construct a multi-epitope vaccine. The vaccine was subjected to in silico analysis, including secondary and tertiary structure prediction, molecular docking, and molecular dynamics simulation. Based on these analyses, a recombinant plasmid, pET-28a(+)-MEVTP, was constructed, and the purified recombinant protein was obtained via nickel column affinity chromatography. In silico immune simulation results suggested that the vaccine could induce specific cellular and humoral immune responses. However, further experimental evaluation of its immunological effects is required to validate the computationally predicted immunogenicity, thereby establishing an experimental basis for its advancement toward translational medical applications and providing critical evidence to support syphilis prevention and control efforts.

Data availability

The datasets analysed during the current study are available in the GenBank repository, https://www.ncbi.nlm.nih.gov/nuccore/AE000520.1.

References

  1. Du, M. et al. Increasing incidence rates of sexually transmitted infections from 2010 to 2019: An analysis of temporal trends by geographical regions and age groups from the 2019 global burden of disease study. BMC. Infect. Dis. 22, 574. https://doi.org/10.1186/s12879-022-07544-7 (2022).

    Google Scholar 

  2. Lu, S. et al. Characterization of Treponema pallidum dissemination in C57BL/6 Mice. Front Immunol. 11, 577129. https://doi.org/10.3389/fimmu.2020.577129 (2020).

    Google Scholar 

  3. Zhang, X. et al. Diagnostic importance of Treponema pallidum Tp0971 in the serological assessment of treatment efficacy for syphilis. Ann. Clin. Microbiol. Antimicrob. 24, 58. https://doi.org/10.1186/s12941-025-00825-4 (2025).

    Google Scholar 

  4. Luo, X. et al. A preliminary study on the proinflammatory mechanisms of Treponema pallidum outer membrane protein Tp92 in human macrophages and HMEC-1 cells. Microb. Pathog. 110, 176–183. https://doi.org/10.1016/j.micpath.2017.06.046 (2017).

    Google Scholar 

  5. Djokic, V., Giacani, L. & Parveen, N. Analysis of host cell binding specificity mediated by the Tp0136 adhesin of the syphilis agent Treponema pallidum subsp. pallidum. PLoS Negl. Trop. Dis. 13, e0007401. https://doi.org/10.1371/journal.pntd.0007401 (2019).

    Google Scholar 

  6. Tang, Y. et al. Investigation of the immune escape mechanism of Treponema pallidum. Infection 51, 305–321. https://doi.org/10.1007/s15010-022-01939-z (2023).

    Google Scholar 

  7. Zuo, W., Xiao, Y., Xiang, Q., Xiao, S. & Xie, Y. Participants in Treponema pallidum pathogenesis: Progress in functional proteins. Front. Immunol. 16, 1632677. https://doi.org/10.3389/fimmu.2025.1632677 (2025).

    Google Scholar 

  8. Ávila-Nieto, C. et al. Syphilis vaccine: Challenges, controversies and opportunities. Front. Immunol. 14, 1126170. https://doi.org/10.3389/fimmu.2023.1126170 (2023).

    Google Scholar 

  9. Liu, Z. et al. Treponema pallidum inhibits CD4+ T-cell proliferation through METAP2: Insights from Mendelian randomization analysis. AMB Express 15, 126. https://doi.org/10.1186/s13568-025-01940-3 (2025).

    Google Scholar 

  10. Lu, S. et al. Treponema pallidum Tp0751 alters the expression of tight junction proteins by promoting bEnd3 cell apoptosis and IL-6 secretion. Int. J. Med. Microbiol. 312, 151553. https://doi.org/10.1016/j.ijmm.2022.151553 (2022).

    Google Scholar 

  11. Jaiswal, A. K., Rodrigues Gomes, L. G., Ferreira Maciel de Oliveira, A., de Castro Soares, S. & Azevedo, V. The critical role of penicillin in syphilis treatment and emerging resistance challenges. Diseases 13, 020041. https://doi.org/10.3390/diseases13020041 (2025).

    Google Scholar 

  12. Chevalier, F. J., Bacon, O., Johnson, K. A. & Cohen, S. E. Syphilis: A review. JAMA 334, 1927–1940. https://doi.org/10.1001/jama.2025.17362 (2025).

    Google Scholar 

  13. Tantalo, L. C. et al. Antimicrobial susceptibility of Treponema pallidum subspecies pallidum: An in-vitro study. Lancet Microbe 4, e994–e1004. https://doi.org/10.1016/s2666-5247(23)00219-7 (2023).

    Google Scholar 

  14. Chen, J., Huang, J., Liu, Z. & Xie, Y. Treponema pallidum outer membrane proteins: Current status and prospects. Pathog. Dis. 80, ftac023. https://doi.org/10.1093/femspd/ftac023 (2022).

    Google Scholar 

  15. Waugh, S. & Cameron, C. E. Syphilis vaccine development: Aligning vaccine design with manufacturing requirements. Hum. Vaccin. Immunother. 20, 2399915. https://doi.org/10.1080/21645515.2024.2399915 (2024).

    Google Scholar 

  16. Jiang, C. et al. Evaluation of FlaB1, FlaB2, FlaB3, and Tp0463 of Treponema pallidum for serodiagnosis of syphilis. Diagn. Microbiol. Infect. Dis. 84, 105–111. https://doi.org/10.1016/j.diagmicrobio.2015.10.005 (2016).

    Google Scholar 

  17. Jiang, J. et al. A comprehensive strategy for the development of a multi-epitope vaccine targeting Treponema pallidum, utilizing heat shock proteins, encompassing the entire process from vaccine design to in vitro evaluation of immunogenicity. Front. Microbiol. 16, 1551437. https://doi.org/10.3389/fmicb.2025.1551437 (2025).

    Google Scholar 

  18. Li, S., Li, W., Jin, Y., Wu, B. & Wu, Y. Advancements in the development of nucleic acid vaccines for syphilis prevention and control. Hum. Vaccin. Immunother. 19, 2234790. https://doi.org/10.1080/21645515.2023.2234790 (2023).

    Google Scholar 

  19. Lithgow, K. V. et al. A defined syphilis vaccine candidate inhibits dissemination of Treponema pallidum subspecies pallidum. Nat. Commun. 8, 14273. https://doi.org/10.1038/ncomms14273 (2017).

    Google Scholar 

  20. Zheng, K. et al. Immunogenicity and protective efficacy against Treponema pallidum in New Zealand rabbits immunized with plasmid DNA encoding flagellin. Emerg. Microbes Infect. 7, 177. https://doi.org/10.1038/s41426-018-0176-0 (2018).

    Google Scholar 

  21. Jun, T. et al. Immunisation with the glycolytic enzyme enolase inhibits dissemination of Treponema pallidum in C57BL/6 mice. Microb. Pathog. 184, 106374. https://doi.org/10.1016/j.micpath.2023.106374 (2023).

    Google Scholar 

  22. Goodswen, S. J., Kennedy, P. J. & Ellis, J. T. A guide to current methodology and usage of reverse vaccinology towards in silico vaccine discovery. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuad004 (2023).

    Google Scholar 

  23. Fan, X. et al. Construction and immunogenicity of a T cell epitope-based subunit vaccine candidate against Mycobacterium tuberculosis. Vaccine 39, 6860–6865. https://doi.org/10.1016/j.vaccine.2021.10.034 (2021).

    Google Scholar 

  24. Wang, C. et al. Evaluation of a tandem Chlamydia psittaci Pgp3 multiepitope peptide vaccine against a pulmonary chlamydial challenge in mice. Microb. Pathog. 147, 104256. https://doi.org/10.1016/j.micpath.2020.104256 (2020).

    Google Scholar 

  25. Yu, J. et al. A novel multi-component protein vaccine ECP001 containing a protein polypeptide antigen nPstS1 riching in T-cell epitopes showed good immunogenicity and protection in mice. Front. Immunol. 14, 1138818. https://doi.org/10.3389/fimmu.2023.1138818 (2023).

    Google Scholar 

  26. Xu, L. et al. Development of a novel multi-epitope vaccine against Ureaplasma urealyticum infection through reverse vaccinology approach. Mol. Divers. https://doi.org/10.1007/s11030-025-11234-2 (2025).

    Google Scholar 

  27. Xu, L. et al. Development of a novel multi-epitope vaccine against Streptococcus anginosus infection via reverse vaccinology approach. Immunology 175, 339–358. https://doi.org/10.1111/imm.13936 (2025).

    Google Scholar 

  28. Isaacs, D. & McVernon, J. Introducing a new group B meningococcus vaccine. BMJ 348, g2415. https://doi.org/10.1136/bmj.g2415 (2014).

    Google Scholar 

  29. Duffy, J. et al. Safety surveillance of bivalent meningococcal group B vaccine, vaccine adverse event reporting system, 2014-2018. Open Forum Infect. Dis. 7, ofaa516. https://doi.org/10.1093/ofid/ofaa516 (2020).

    Google Scholar 

  30. Basmenj, E. R. et al. Computational epitope-based vaccine design with bioinformatics approach; A review. Heliyon 11, e41714. https://doi.org/10.1016/j.heliyon.2025.e41714 (2025).

    Google Scholar 

  31. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410. https://doi.org/10.1016/s0022-2836(05)80360-2 (1990).

    Google Scholar 

  32. Clem, A. S. Fundamentals of vaccine immunology. J. Glob. Infect. Dis. 3, 73–78. https://doi.org/10.4103/0974-777x.77299 (2011).

    Google Scholar 

  33. Saha, S. & Raghava, G. P. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins 65, 40–48. https://doi.org/10.1002/prot.21078 (2006).

    Google Scholar 

  34. Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8, 4. https://doi.org/10.1186/1471-2105-8-4 (2007).

    Google Scholar 

  35. Gupta, S. et al. In silico approach for predicting toxicity of peptides and proteins. PLoS ONE 8, e73957. https://doi.org/10.1371/journal.pone.0073957 (2013).

    Google Scholar 

  36. Dimitrov, I., Bangov, I., Flower, D. R. & Doytchinova, I. AllerTOP v.2--A server for in silico prediction of allergens. J. Mol. Model. 20, 2278. https://doi.org/10.1007/s00894-014-2278-5 (2014).

    Google Scholar 

  37. Yan, Z. et al. Next-generation IEDB tools: A platform for epitope prediction and analysis. Nucleic Acids Res. 52, W526-w532. https://doi.org/10.1093/nar/gkae407 (2024).

    Google Scholar 

  38. Calis, J. J. et al. Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 9, e1003266. https://doi.org/10.1371/journal.pcbi.1003266 (2013).

    Google Scholar 

  39. Wang, P. et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics 11, 568. https://doi.org/10.1186/1471-2105-11-568 (2010).

    Google Scholar 

  40. Dhanda, S. K., Vir, P. & Raghava, G. P. Designing of interferon-gamma inducing MHC class-II binders. Biol. Direct. 8, 30. https://doi.org/10.1186/1745-6150-8-30 (2013).

    Google Scholar 

  41. Roohparvar Basmenj, E., Omidvar, B., Kiumarsy, A., Izadkhah, H. & Ghiabi, S. Design of a multi-epitope-based peptide vaccine against the SARS-CoV-2 Omicron variant using bioinformatics approach. J. Biomol. Struct. Dyn. 42, 7945–7956. https://doi.org/10.1080/07391102.2023.2241926 (2024).

    Google Scholar 

  42. Bui, H. H. et al. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 7, 153. https://doi.org/10.1186/1471-2105-7-153 (2006).

    Google Scholar 

  43. Maupetit, J., Derreumaux, P. & Tufféry, P. A fast method for large-scale de novo peptide and miniprotein structure prediction. J. Comput. Chem. 31, 726–738. https://doi.org/10.1002/jcc.21365 (2010).

    Google Scholar 

  44. Berman, H. M. The protein data bank. Nucleic Acids Res. 28, 235–242. https://doi.org/10.1093/nar/28.1.235 (2000).

    Google Scholar 

  45. Weng, G. et al. HawkDock: A web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 47, W322-w330. https://doi.org/10.1093/nar/gkz397 (2019).

    Google Scholar 

  46. Tan, C. et al. Development of multi-epitope mRNA vaccine against Clostridioides difficile using reverse vaccinology and immunoinformatics approaches. Synth. Syst. Biotechnol. 9, 667–683. https://doi.org/10.1016/j.synbio.2024.05.008 (2024).

    Google Scholar 

  47. Guo, H. et al. Effect of flexible linker length on the activity of fusion protein 4-coumaroyl-CoA ligase::stilbene synthase. Mol. Biosyst. 13, 598–606. https://doi.org/10.1039/c6mb00563b (2017).

    Google Scholar 

  48. Zhao, T. et al. Vaccine adjuvants: Mechanisms and platforms. Signal Transduct. Target. Ther. 8, 283. https://doi.org/10.1038/s41392-023-01557-7 (2023).

    Google Scholar 

  49. Arai, R., Ueda, H., Kitayama, A., Kamiya, N. & Nagamune, T. Design of the linkers which effectively separate domains of a bifunctional fusion protein. Protein Eng. 14, 529–532. https://doi.org/10.1093/protein/14.8.529 (2001).

    Google Scholar 

  50. Liu, J. et al. A multi-epitope subunit vaccine providing broad cross-protection against diverse serotypes of Streptococcus suis. npj Vaccines 9, 216. https://doi.org/10.1038/s41541-024-01015-7 (2024).

    Google Scholar 

  51. Hebditch, M., Carballo-Amador, M. A., Charonis, S., Curtis, R. & Warwicker, J. Protein-Sol: A web tool for predicting protein solubility from sequence. Bioinformatics 33, 3098–3100. https://doi.org/10.1093/bioinformatics/btx345 (2017).

    Google Scholar 

  52. Conchillo-Solé, O. et al. AGGRESCAN: A server for the prediction and evaluation of “hot spots” of aggregation in polypeptides. BMC Bioinformatics 8, 65. https://doi.org/10.1186/1471-2105-8-65 (2007).

    Google Scholar 

  53. Mészáros, B., Erdos, G. & Dosztányi, Z. IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 46, W329-w337. https://doi.org/10.1093/nar/gky384 (2018).

    Google Scholar 

  54. Wilkins, M. R. et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 112, 531–552. https://doi.org/10.1385/1-59259-584-7:531 (1999).

    Google Scholar 

  55. Nielsen, H., Teufel, F., Brunak, S. & von Heijne, G. SignalP: The evolution of a web server. Methods Mol. Biol. 2836, 331–367. https://doi.org/10.1007/978-1-0716-4007-4_17 (2024).

    Google Scholar 

  56. Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202. https://doi.org/10.1006/jmbi.1999.3091 (1999).

    Google Scholar 

  57. Geourjon, C. & Deléage, G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput. Appl. Biosci. 11, 681–684. https://doi.org/10.1093/bioinformatics/11.6.681 (1995).

    Google Scholar 

  58. Adam, K. M. Immunoinformatics approach for multi-epitope vaccine design against structural proteins and ORF1a polyprotein of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Trop Dis Travel Med Vaccines 7, 22. https://doi.org/10.1186/s40794-021-00147-1 (2021).

    Google Scholar 

  59. Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500. https://doi.org/10.1038/s41586-024-07487-w (2024).

    Google Scholar 

  60. Pourseif, M. M., Baradaran Hosseini, S. A., Khoshraftar, S. H. & Omidi, Y. The role of bioinformatics algorithms in modern biopharmaceutical design: Progress, challenges, and future perspectives. Bioimpacts 15, 33072. https://doi.org/10.34172/bi.33072 (2025).

    Google Scholar 

  61. Heo, L., Park, H. & Seok, C. GalaxyRefine: Protein structure refinement driven by side-chain repacking. Nucleic Acids Res. 41, W384-388. https://doi.org/10.1093/nar/gkt458 (2013).

    Google Scholar 

  62. Wiederstein, M. & Sippl, M. J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 35, W407-410. https://doi.org/10.1093/nar/gkm290 (2007).

    Google Scholar 

  63. Sippl, M. J. Recognition of errors in three-dimensional structures of proteins. Proteins 17, 355–362. https://doi.org/10.1002/prot.340170404 (1993).

    Google Scholar 

  64. Colovos, C. & Yeates, T. O. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci. 2, 1511–1519. https://doi.org/10.1002/pro.5560020916 (1993).

    Google Scholar 

  65. Kang, S. M. & Compans, R. W. Host responses from innate to adaptive immunity after vaccination: Molecular and cellular events. Mol. Cells 27, 5–14. https://doi.org/10.1007/s10059-009-0015-1 (2009).

    Google Scholar 

  66. Huang, T. et al. MicroRNA-101-3p downregulates TLR2 expression, leading to reduction in cytokine production by Treponema pallidum-stimulated macrophages. J. Invest. Dermatol. 140, 1566-1575.e1561. https://doi.org/10.1016/j.jid.2019.12.012 (2020).

    Google Scholar 

  67. Peng, R. R., Shang, S. X., Zhao, L. S. & Long, F. Q. MiR-216a-5p-containing exosomes suppress rTp17-induced inflammatory response by targeting TLR4. Biosci. Rep. https://doi.org/10.1042/bsr20190686 (2019).

  68. Laskowski, R. A., Jabłońska, J., Pravda, L., Vařeková, R. S. & Thornton, J. M. PDBsum: Structural summaries of PDB entries. Protein Sci. 27, 129–134. https://doi.org/10.1002/pro.3289 (2018).

    Google Scholar 

  69. Abraham, M. J. et al. 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 (2015).

    Google Scholar 

  70. Van Der Spoel, D. et al. GROMACS: Fast, flexible, and free. J. Comput. Chem. 26, 1701–1718. https://doi.org/10.1002/jcc.20291 (2005).

    Google Scholar 

  71. López-Blanco, J. R., Aliaga, J. I., Quintana-Ortí, E. S. & Chacón, P. iMODS: Internal coordinates normal mode analysis server. Nucleic Acids Res. 42, W271-276. https://doi.org/10.1093/nar/gku339 (2014).

    Google Scholar 

  72. Ponomarenko, J. et al. ElliPro: A new structure-based tool for the prediction of antibody epitopes. BMC Bioinformatics 9, 514. https://doi.org/10.1186/1471-2105-9-514 (2008).

    Google Scholar 

  73. Puigbò, P., Guzmán, E., Romeu, A. & Garcia-Vallvé, S. OPTIMIZER: A web server for optimizing the codon usage of DNA sequences. Nucleic Acids Res. 35, W126-131. https://doi.org/10.1093/nar/gkm219 (2007).

    Google Scholar 

  74. Quax, T. E., Claassens, N. J., Söll, D. & van der Oost, J. Codon Bias as a means to fine-tune gene expression. Mol Cell. 59, 149–161. https://doi.org/10.1016/j.molcel.2015.05.035 (2015).

    Google Scholar 

  75. Suneesh, N. S. et al. Reverse vaccinology-based design of multivalent multiepitope mRNA vaccines targeting key viral proteins of herpes simplex virus type-2. Front Immunol. 16, 1586271. https://doi.org/10.3389/fimmu.2025.1586271 (2025).

    Google Scholar 

  76. Reshetnikov, V. et al. Untranslated region sequences and the efficacy of mRNA vaccines against tuberculosis. Int. J. Mol. Sci. https://doi.org/10.3390/ijms25020888 (2024).

    Google Scholar 

  77. Nance, K. D. & Meier, J. L. Modifications in an emergency: The role of N1-Methylpseudouridine in COVID-19 vaccines. ACS Cent. Sci. 7, 748–756. https://doi.org/10.1021/acscentsci.1c00197 (2021).

    Google Scholar 

  78. Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc Natl Acad Sci U S A. 101, 7287–7292. https://doi.org/10.1073/pnas.0401799101 (2004).

    Google Scholar 

  79. Rapin, N., Lund, O., Bernaschi, M. & Castiglione, F. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLoS ONE 5, e9862. https://doi.org/10.1371/journal.pone.0009862 (2010).

    Google Scholar 

  80. Rapin, N., Lund, O. & Castiglione, F. Immune system simulation online. Bioinformatics 27, 2013–2014. https://doi.org/10.1093/bioinformatics/btr335 (2011).

    Google Scholar 

  81. Xiao, Y. et al. Molecular subtyping and surveillance of resistance genes in Treponema pallidum DNA from patients with secondary and latent syphilis in Hunan, China. Sex. Transm. Dis. 43, 310–316. https://doi.org/10.1097/olq.0000000000000445 (2016).

    Google Scholar 

  82. Luo, Y., Xie, Y. & Xiao, Y. Laboratory diagnostic tools for syphilis: Current status and future prospects. Front. Cell Infect. Microbiol. 10, 574806. https://doi.org/10.3389/fcimb.2020.574806 (2020).

    Google Scholar 

  83. Hagymasi, A. T., Dempsey, J. P. & Srivastava, P. K. Heat-shock proteins. Curr. Protoc. 2, e592. https://doi.org/10.1002/cpz1.592 (2022).

    Google Scholar 

  84. Dreiza, C. M. et al. The small heat shock protein, HSPB6, in muscle function and disease. Cell Stress Chaperones 15, 1–11. https://doi.org/10.1007/s12192-009-0127-8 (2010).

    Google Scholar 

  85. Zininga, T., Ramatsui, L. & Shonhai, A. Heat shock proteins as immunomodulants. Molecules https://doi.org/10.3390/molecules23112846 (2018).

    Google Scholar 

  86. Haynes, A. M., Godornes, C., Ke, W. & Giacani, L. Evaluation of the protective ability of the Treponema pallidum subsp. pallidum Tp0126 OmpW homolog in the rabbit model of syphilis. Infect. Immun. https://doi.org/10.1128/iai.00323-19 (2019).

    Google Scholar 

  87. Xu, M. et al. Two potential syphilis vaccine candidates inhibit dissemination of Treponema pallidum. Front. Immunol. 12, 759474. https://doi.org/10.3389/fimmu.2021.759474 (2021).

    Google Scholar 

  88. He, Y. et al. Immunization with Tp0954, an adhesin of Treponema pallidum, provides protective efficacy in the rabbit model of experimental syphilis. Front. Immunol. 14, 1130593. https://doi.org/10.3389/fimmu.2023.1130593 (2023).

    Google Scholar 

  89. Houston, S. et al. The multifunctional role of the pallilysin-associated Treponema pallidum protein, Tp0750, in promoting fibrinolysis and extracellular matrix component degradation. Mol. Microbiol. 91, 618–634. https://doi.org/10.1111/mmi.12482 (2014).

    Google Scholar 

  90. Houston, S. et al. Bifunctional role of the Treponema pallidum extracellular matrix binding adhesin Tp0751. Infect. Immun. 79, 1386–1398. https://doi.org/10.1128/iai.01083-10 (2011).

    Google Scholar 

  91. Primus, S., Rocha, S. C., Giacani, L. & Parveen, N. Identification and functional assessment of the first placental adhesin of Treponema pallidum that may play critical role in congenital syphilis. Front. Microbiol. 11, 621654. https://doi.org/10.3389/fmicb.2020.621654 (2020).

    Google Scholar 

  92. Giri, T. K. & Tollefsen, D. M. Placental dermatan sulfate: Isolation, anticoagulant activity, and association with heparin cofactor II. Blood 107, 2753–2758. https://doi.org/10.1182/blood-2005-09-3755 (2006).

    Google Scholar 

  93. Suga, N. et al. Heparin/heparan sulfate/CD44-v3 enhances cell migration in term placenta-derived immortalized human trophoblast cells. Biol. Reprod. 86, 131–138. https://doi.org/10.1095/biolreprod.111.093690 (2012).

    Google Scholar 

  94. Li, W. et al. Research progress on the mechanism of Treponema pallidum breaking through placental barrier. Microb. Pathog. 185, 106392. https://doi.org/10.1016/j.micpath.2023.106392 (2023).

    Google Scholar 

  95. Li, Q. L. et al. Screening the B- and T-cell epitope map of TP0136 and exploring their effect in a Treponema pallidum rabbit model. Biomed. Pharmacother. 167, 115628. https://doi.org/10.1016/j.biopha.2023.115628 (2023).

    Google Scholar 

  96. Tomson, F. L., Conley, P. G., Norgard, M. V. & Hagman, K. E. Assessment of cell-surface exposure and vaccinogenic potentials of Treponema pallidum candidate outer membrane proteins. Microbes Infect. 9, 1267–1275. https://doi.org/10.1016/j.micinf.2007.05.018 (2007).

    Google Scholar 

  97. Van Voorhis, W. C. et al. Serodiagnosis of syphilis: Antibodies to recombinant Tp0453, Tp92, and Gpd proteins are sensitive and specific indicators of infection by Treponema pallidum. J. Clin. Microbiol. 41, 3668–3674. https://doi.org/10.1128/jcm.41.8.3668-3674.2003 (2003).

    Google Scholar 

  98. Rappuoli, R., Bottomley, M. J., D’Oro, U., Finco, O. & De Gregorio, E. Reverse vaccinology 2.0: Human immunology instructs vaccine antigen design. J. Exp. Med. 213, 469–481. https://doi.org/10.1084/jem.20151960 (2016).

    Google Scholar 

  99. Xie, Y. et al. Treponema pallidum flagellin FlaA2 induces IL-6 secretion in THP-1 cells via the Toll-like receptor 2 signaling pathway. Mol. Immunol. 81, 42–51. https://doi.org/10.1016/j.molimm.2016.11.005 (2017).

    Google Scholar 

  100. Shantier, S. W. et al. Novel multi epitope-based vaccine against monkeypox virus: Vaccinomic approach. Sci. Rep. 12, 15983. https://doi.org/10.1038/s41598-022-20397-z (2022).

    Google Scholar 

  101. Aiman, S. et al. Multi-epitope chimeric vaccine design against emerging Monkeypox virus via reverse vaccinology techniques- A bioinformatics and immunoinformatics approach. Front. Immunol. 13, 985450. https://doi.org/10.3389/fimmu.2022.985450 (2022).

    Google Scholar 

  102. Tan, T. et al. mRNA vaccine - A new cancer treatment strategy. Curr. Cancer Drug Targets 23, 669–681. https://doi.org/10.2174/1568009623666230222124424 (2023).

    Google Scholar 

  103. Jin, Y., Hou, C., Li, Y., Zheng, K. & Wang, C. mRNA vaccine: How to meet the challenge of SARS-CoV-2. Front. Immunol. 12, 821538. https://doi.org/10.3389/fimmu.2021.821538 (2021).

    Google Scholar 

  104. Roohparvar Basmenj, E. et al. A novel approach to design a multiepitope peptide as a vaccine candidate for Bordetella pertussis. J. Biomol. Struct. Dyn. 42, 13738–13750. https://doi.org/10.1080/07391102.2023.2278081 (2024).

    Google Scholar 

  105. Koupaei, F. N. et al. Design of a multi-epitope vaccine candidate against Vibrio cholerae. Sci. Rep. 15, 11033. https://doi.org/10.1038/s41598-025-90598-9 (2025).

    Google Scholar 

Download references

Funding

This research was funded by the Natural Science Foundation of Hunan Province grant number: 2024JJ7457, 2023JJ30534, and 2023JJ50150.

Author information

Author notes

  1. Hongmei Tang and Zhixi Chen have the first authors.

Authors and Affiliations

  1. Department of Dermatology and Venereology, The First Affiliated Hospital, Hengyang Medical College, University of South China, Hengyang, 421001, Hunan, China

    Hongmei Tang, Zhixi Chen & Xiaoliu Wang

  2. Department of Clinical Laboratory, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China

    Yafeng Xie

  3. Department of Blood Transfusion, The First Affiliated Hospital, Hengyang Medical College, University of South China, Hengyang, 421001, Hunan, China

    Zhixi Chen

  4. Department of Pediatrics, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, 421001, Hunan, China

    Hongxia Yan

  5. Institute of Pathogenic Biology, Basic Medical School, Hengyang Medical College, Key Laboratory of Special Pathogen Prevention and Control of Hunan Province, University of South China, Hengyang, 421001, China

    Hongmei Tang, Zhen He & Ranhui Li

Authors
  1. Hongmei Tang
    View author publications

    Search author on:PubMed Google Scholar

  2. Zhixi Chen
    View author publications

    Search author on:PubMed Google Scholar

  3. Hongxia Yan
    View author publications

    Search author on:PubMed Google Scholar

  4. Zhen He
    View author publications

    Search author on:PubMed Google Scholar

  5. Ranhui Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Yafeng Xie
    View author publications

    Search author on:PubMed Google Scholar

  7. Xiaoliu Wang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

HT Writing-review and editing, Writing-original draft, Visualization, Validation. ZC Data curation, Conceptualization, Formal analysis. ZH Methodology, Investigation. HY Formal analysis, Data curation. RL Visualization, Methodology. YX Writing -review and editing, Funding acquisition. XW Supervision, Investigation, Formal analysis, Funding acquisition. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yafeng Xie or Xiaoliu Wang.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (download PDF )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tang, H., Chen, Z., Yan, H. et al. Designing a novel multiepitope vaccine candidate against Treponema pallidum via adhesins using reverse vaccinology. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45084-1

Download citation

  • Received: 19 November 2025

  • Accepted: 17 March 2026

  • Published: 01 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-45084-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Syphilis
  • Adhesins
  • Treponema pallidum
  • Multiepitope vaccine
  • Reverse vaccinology
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research