Immunoinformatic design of a multiepitope vaccine construct based on proteins from Mycobacterium tuberculosis KZN 1435
Tuberculosis (TB) caused by Mycobacterium tuberculosis remains a major global health burden, particularly with the emergence of multidrug-resistant (MDR) strains such as KZN 1435. In this study, a strain-specific multiepitope subunit vaccine candidate was rationally designed using an integrated immunoinformatic pipeline to support targeted vaccine development against MDR-TB. Candidate proteins from the M. tuberculosis KZN 1435 proteome were screened for secretory potential, transmembrane topology, and non-homology with human proteins. A total of 84 non-redundant secretory candidate proteins were retained after the initial secretory-protein filtering step and were further evaluated for downstream epitope prediction. Predicted 12-mer B-cell, cytotoxic T lymphocyte, and helper T lymphocyte epitopes were assessed for antigenicity, allergenicity, toxicity, and HLA-binding profiles, resulting in eight high-confidence epitope-protein entries corresponding to six non-redundant peptide sequences. After removal of duplicated peptide sequences and alignment with the final vaccine architecture used for downstream analyses, five non-redundant epitopes were assembled with an N-terminal adjuvant and optimized linkers to generate a 126-residue multiepitope construct with favorable predicted physicochemical properties, hydrophilicity, solubility, and theoretical immunogenic potential. Tertiary structure modeling suggested an acceptable folded conformation, while molecular docking indicated compatible interactions with TLR2 and TLR4 immune receptors. Codon optimization further supported the theoretical feasibility of recombinant expression in Escherichia coli. This strain-focused vaccine construct represents a promising computational candidate that requires further in vitro and in vivo validation before translational application against drug-sensitive and MDR M. tuberculosis infections.
Prasetya YA, Setyawati P, Kok T, Wahjudi M. Immunoinformatic design of a multiepitope vaccine construct based on proteins from Mycobacterium tuberculosis KZN 1435. J Appl Biol Biotech 2026. Article in Press. http://doi.org/10.7324/jabb.2026.314726
1. World Health Organization. Global tuberculosis report 2025. World Health Organization, Geneva, Switzerland, 2025.
2. Strengthening TB surveillance to accelerate Indonesia’s path to elimination [Internet]. Available via: https://www.who.int/indonesia/news/detail/01-07-2025-strengthening-tb-surveillance-to-accelerate-indonesia-s-path-to-elimination (Accessed 04 July 2026)
3. Floyd K, Glaziou P, Zumla A, Raviglione M. The global tuberculosis epidemic and progress in care, prevention, and research: an overview in year 3 of the End TB era. Lancet Respir Med 2018;6(4):299–314; doi: https://doi.org/10.1016/S2213-2600(18)30057-2
4. Chandra P, Grigsby SJ, Philips JA. Immune evasion and provocation by Mycobacterium tuberculosis. Nat Rev Microbiol 2022;20(12):750– 66; doi: https://doi.org/10.1038/s41579-022-00763-4
5. Schmidt AC, Fairlie L, Hellström E, Luabeya Kany Kany A, Middelkoop K, Naidoo K, et al. BCG revaccination for the prevention of Mycobacterium tuberculosis infection. N Engl J Med 2025;392(18):1789–800; doi: https://doi.org/10.1056/NEJMoa2412381
6. Ali A, Luqman Ali S. A stable mRNA-based novel multi-epitope vaccine designs against infectious heartland virus by integrated immunoinformatics and reverse vaccinology approaches. Viral Immunol 2025;38(3):73–87; doi: https://doi.org/10.1089/vim.2025.0004
7. Akhtar N, Magdaleno JSL, Ranjan S, Wani AK, Grewal RK, Oliva R, et al. Secreted aspartyl proteinases targeted multi-epitope vaccine design for Candida dubliniensis using immunoinformatics. Vaccines 2023;11(2):364; doi: https://doi.org/10.3390/vaccines11020364
8. Atapour A, Vosough P, Jafari S, Sarab GA. A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach. Sci Rep 2022;12(1):11683; doi: https://doi.org/10.1038/s41598-022-15956-3
9. Cohen KA, Abeel T, Manson Mcguire A, Desjardins CA, Munsamy V, Shea TP, et al. Evolution of extensively drug-resistant tuberculosis over four decades: whole genome sequencing and dating analysis of Mycobacterium tuberculosis isolates from KwaZulu-Natal. PLoS Med 2015;12(9):e1001880; doi: https://doi.org/10.1371/journal.pmed.1001880
10. Alzarea SI. Identification and construction of a multi-epitopes vaccine design against Klebsiella aerogenes: molecular modeling study. Sci Rep 2022;12(1):14402; doi: https://doi.org/10.1038/s41598-022-18610-0
11. Alshammari A, Alharbi M, Alghamdi A, Alharbi SA, Ashfaq UA, Tahir Ul Qamar M, et al. Computer-aided multi-epitope vaccine design against Enterobacter xiangfangensis. Int J Environ Res Public Health 2022;19(13):7723; doi: https://doi.org/10.3390/ijerph19137723
12. Frank K, Sippl MJ.High-performance signal peptide prediction based on sequence alignment techniques. Bioinformatics 2008;24(19):2172–6; doi: https://doi.org/10.1093/bioinformatics/btn422
13. Savojardo C, Martelli PL, Fariselli P, Casadio R. DeepSig: deep learning improves signal peptide detection in proteins. Bioinformatics 2018;34(10):1690–6; btx818
14. Kall L, Krogh A, Sonnhammer ELL. Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server. Nucleic Acids Res 2007;35:W429–432; doi: https://doi.org/10.1093/nar/gkm256
15. Teufel F, Almagro Armenteros JJ, Johansen AR, Gíslason MH, Pihl SI, Tsirigos KD, et al. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 2022;40(7):1023–5; doi: https://doi.org/10.1038/s41587-021-01156- 3
16. Almagro Armenteros JJ, Salvatore M, Emanuelsson O, Winther O, Von Heijne G, Elofsson A, et al. Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2019;2(5):e201900429; doi: https://doi.org/10.26508/lsa.201900429
17. Hallgren J, Tsirigos KD, Pedersen MD, Almagro Armenteros JJ, Marcatili P, Nielsen H, et al. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks [Internet]. Bioinformatics 2022;2026; doi: https://doi.org/10.1101/2022.04.08.487609
18. Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins Struct Funct Bioinforma 2006;65(1):40–8; doi: https://doi.org/10.1002/prot.21078
19. Reynisson B, Alvarez B, Paul S, Peters B, Nielsen M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 2020;48(1):W449– 54; doi: https://doi.org/10.1093/nar/gkaa379
20. Wang P, Sidney J, Kim Y, Sette A, Lund O, Nielsen M, et al. Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinf 2010;11(1):568; doi: https://doi.org/10.1186/1471-2105-11- 568
21. Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinf 2007;8(1):4; doi: https://doi.org/10.1186/1471-2105-8-4
22. Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP: allergenicity prediction by descriptor fingerprints. Bioinformatics 2014;30(6):846–51; doi: https://doi.org/10.1093/bioinformatics/btt619
23. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GPS. In silico approach for predicting toxicity of peptides and proteins. PLoS One 2013;8(9):e73957; doi: https://doi.org/10.1371/journal.pone.0073957
24. Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X, Peters B, et al. The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front Immunol 2017;8; doi: https://doi.org/10.3389/fimmu.2017.00278
25. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server [Internet]. In: Walker JM (ed.). The proteomics protocols handbook, Humana Press, Totowa, NJ, 2005, p 571; doi: https://doi.org/10.1385/1-59259-890-0:571
26. Hon J, Marusiak M, Martinek T, Kunka A, Zendulka J, Bednar D, et al. SoluProt: prediction of soluble protein expression in Escherichia coli. Bioinformatics 2021;37(1):23–8; doi: https://doi.org/10.1093/bioinformatics/btaa1102
27. Hebditch M, Carballo-Amador MA, Charonis S, Curtis R, Warwicker J.Protein–Sol: a web tool for predicting protein solubility from sequence. Bioinformatics 2017;33(19):3098–100; doi: https://doi.org/10.1093/bioinformatics/btx345
28. Bhandari BK, Gardner PP, Lim CS. Solubility-weighted index: fast and accurate prediction of protein solubility. Bioinformatics 2020;36(18):4691–8; doi: https://doi.org/10.1093/bioinformatics/btaa578
29. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods 2015;12(1):7–8; doi: https://doi.org/10.1038/nmeth.3213
30. Studer G, Biasini M, Schwede T. Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane). Bioinformatics 2014;30(17):i505–11; doi: https://doi.org/10.1093/bioinformatics/btu457
31. Liu Y, Grimm M, Dai WT, Hou MC, Xiao ZX, Cao Y. CB-Dock: a web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacol Sin 2020;41(1):138–44; doi: https://doi.org/10.1038/s41401-019-0228-6
32. Adasme MF, Linnemann KL, Bolz SN, Kaiser F, Salentin S, Haupt VJ, et al. PLIP 2021: expanding the scope of the protein– ligand interaction profiler to DNA and RNA. Nucleic Acids Res 2021;49(1):W530–534; doi: https://doi.org/10.1093/nar/gkab294
33. Grote A, Hiller K, Scheer M, Munch R, Nortemann B, Hempel DC, et al. JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005;33:W526–31; doi: https://doi.org/10.1093/nar/gki376
34. Ferris LK, Mburu YK, Mathers AR, Fluharty ER, Larregina AT, Ferris RL, et al. Human beta-defensin 3 induces maturation of human langerhans cell–like dendritic cells: an antimicrobial peptide that functions as an endogenous adjuvant. J Invest Dermatol 2013;133(2):460–8; doi: https://doi.org/10.1038/jid.2012.319
35. Chen X, Zaro JL, Shen WC. Fusion protein linkers: property, design and functionality. Adv Drug Deliv Rev 2013;65(10):1357–69; doi: https://doi.org/10.1016/j.addr.2012.09.039
36. Shah M, Rafiq S, Woo HG. Challenges and considerations in multi-epitope vaccine design surrounding toll-like receptors. Trends Pharmacol Sci 2024;45(12):1104–18; doi: https://doi.org/10.1016/j.tips.2024.10.013
37. Bolivar F, Rodriguez RL, Greene PJ, Betlach MC, Heyneker HL, Boyer HW, et al. Construction and characterization of new cloning vehicles. II. A multipurpose cloning system. Gene 1977;2:95–113.
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