A Novel Multiepitope Vaccine for Jembrana Disease: Immunoinformatics, Structural Analysis, Molecular Docking, and Molecular Dynamics
Fatimah Fatimah(1), Syahputra Wibowo(2), Dini Wahyu Kartika Sari(3), Rarastoeti Pratiwi(4), Asmarani Kusumawati(5*)
(1) Study Program of Doctor in Biotechnology, Graduate School, University of Gadjah Mada, Teknika Utara, Yogyakarta 55281, Indonesia; Department of Medical Laboratory Technology, STIKES Karya Putra Bangsa, Jl. Raya Tulungagung - Blitar Km. 4, Tulungagung 66291, Indonesia
(2) Eijkman Research Center for Molecular Biology, National Research and Innovation Agency, Meatpro Building, Soekarno Science and Technology Area (KST), Jl. Raya Jakarta-Bogor Km. 46 Cibinong, Bogor 16911, Indonesia
(3) Study Program of Doctor in Biotechnology, Graduate School, University of Gadjah Mada, Teknika Utara, Yogyakarta 55281, Indonesia; Department of Fisheries, Faculty of Agriculture, Universitas Gadjah Mada, Jl. Flora, Yogyakarta 55821, Indonesia
(4) Study Program of Doctor in Biotechnology, Graduate School, University of Gadjah Mada, Teknika Utara, Yogyakarta 55281, Indonesia; Faculty of Biology, Universitas Gadjah Mada, Jl. Teknika Selatan, Sekip Utara, Yogyakarta 55281, Indonesia
(5) Study Program of Doctor in Biotechnology, Graduate School, University of Gadjah Mada, Teknika Utara, Yogyakarta 55281, Indonesia; Department of Reproduction and Obstetrics, Faculty of Veterinary Medicine, Universitas Gadjah Mada, Jl. Fauna No. 2, Karangmalang, Yogyakarta, 55281, Indonesia
(*) Corresponding Author
Abstract
Jembrana disease, caused by the Jembrana virus, leads to high mortality (30%) and abortion (49%) in cattle, making vaccination essential. In this study, a multiepitope vaccine was designed using immunogenic TM and CA proteins. Predicted cytotoxic T lymphocyte, helper T lymphocyte, and linear B-cell epitopes were linked with flexible linkers, and the 50S L7/L12 ribosomal protein was added as a TLR4 agonist. In silico analysis confirmed the construct as non-allergenic, antigenic, and thermostable (aliphatic index 85.44), with 94.2% of residues in favored Ramachandran regions. Docking analysis revealed strong binding to TLR4 (−66 kcal/mol), and molecular dynamics simulations validated the structural stability. Immune simulations revealed increased antigen and antibody levels (IgM early, IgG1/IgG2 after day 15), progressive CD4+ T-helper expansion, transient CD8+ T-cell peaks, elevated IFN-γ and IL-2, and strong dendritic cell activation through MHC I and II pathways. These findings indicate the vaccine effectively stimulates humoral and cellular responses, supporting its potential as a promising candidate against the Jembrana virus.
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[1] Meles, D.K., Khairullah, A.R., Utama, S., Wurlina, W., Mulyati, S., Mustofa, I., Rimayanti, R., Lestari, T.D., Moses, I.B., Wibowo, S., Wardhani, B.W.K., Kurniasih, D.A.A., Kusala, M.K.J., Ahmad, R.Z., Fauziah, I., Wasito, W., and Akintunde, A.O., 2025, Jembrana disease in Indonesia: An updated review, Open Vet. J., 15 (3), 1091–1100.
[2] Desport, M., Stewart, M., Sheridan, C., Ditcham, W., Setiyaningsih, S., Tenaya, W.M., Hartaningsih, N., and Wilcox, G., 2005, Recombinant Jembrana disease virus gag proteins identify several different antigenic domains but do not facilitate serological differentiation of JDV and nonpathogenic bovine lentiviruses, J. Virol. Methods, 124 (1-2), 135–142.
[3] Kusumawati, A., Wanahari, T.A., Astuti, P., Kurniasih, M., Mappakaya, B.A., and Wuryastuty, H., 2015, Vaccine against Jembrana disease virus infection: A summary of findings, Am. J. Immunol., 11 (3), 68–73.
[4] Ditcham, W.G., Lewis, J.R., Dobson, R.J., Hartaningsih, N., Wilcox, G.E., and Desport, M., 2009, Vaccination reduces the viral load and the risk of transmission of Jembrana disease virus in Bali cattle, Virology, 386 (2), 317–324.
[5] Unsunnidhal, L., Ishak, J., and Kusumawati, A., 2019, Expression of gag-CA gene of Jembrana disease virus with cationic liposomes and chitosan nanoparticle delivery systems as DNA vaccine candidates, Trop. Life Sci. Res., 30 (3), 15–36.
[6] Lee, J., Arun Kumar, S., Jhan, Y.Y., and Bishop, C.J., 2018, Engineering DNA vaccines against infectious diseases, Acta Biomater., 80, 31–47.
[7] Zhang, L., 2018, Multiepitope vaccines: A promising strategy against tumors and viral infections, Cell. Mol. Immunol., 15 (2), 182–184.
[8] Behmard, E., Abdulabbas, H.T., Abdalkareem Jasim, S., Najafipour, S., Ghasemian, A., Farjadfar, A., Barzegari, E., Kouhpayeh, A., and Abdolmaleki, P., 2022, Design of a novel multiepitope vaccine candidate against hepatitis C virus using structural and nonstructural proteins: an immunoinformatics approach, PLoS One, 17 (8), e0272582.
[9] Li, M., Zhu, Y., Niu, C., Xie, X., Haimiti, G., Guo, W., Yu, M., Chen, Z., Ding, J., and Zhang, F., 2022, Design of a multiepitope vaccine candidate against Brucella melitensis, Sci. Rep., 12 (1), 10146.
[10] Siddiki, A.Z., Alam, S., Tithi, F.A., Hoque, S.F., Sajib, E.H., Bin Hossen, F.F., and Hossain, M.A., 2023, Construction of a multiepitope in silico vaccine against Anaplasma marginale using immunoinformatics approach, Biocatal. Agric. Biotechnol., 50, 102706.
[11] Uddin, M.B., Tanni, F.Y., Hoque, S.F., Sajib, E.H., Faysal, M.A., Rahman, M.A., Galib, A., Al Emon, A., Hossain, M.M., Hasan, M., and Ahmed, S.S.U., 2022, A candidate multiepitope vaccine against lumpy skin disease, Transboundary Emerging Dis., 69 (6), 3548–3561.
[12] Rahimnahal, S., Yousefizadeh, S., and Mohammadi, Y., 2023, Novel multiepitope vaccine against bovine brucellosis: Approach from immunoinformatics to expression, J. Biomol. Struct. Dyn., 41 (24), 15460–15484.
[13] Riaz, R., Zahid, S., and Khan, M.S., 2024, Designing an epitope-based vaccine against bovine viral diarrhea using immuno-informatics, Pak. Vet. J., 44 (2), 465–475.
[14] Vita, R., Blazeska, N., Marrama, D., IEDB Curation Team Members, Duesing, S., Bennett, J., Greenbaum, J., De Almeida Mendes, M., Mahita, J., Wheeler, D.K., Cantrell, J.R., Overton, J.A., Natale, D.A., Sette, A., and Peters, B., 2025, The immune epitope database (IEDB): 2024 update, Nucleic Acids Res., 53 (D1), D436–D443.
[15] Susianti, S., Bahri, S., Hadi, S., Setiawansyah, A., Rachmadi, L., Fadilah, I., and Ratna, M.G., 2025, Integrating the network pharmacology and molecular docking confirmed with in vitro toxicity to reveal potential mechanism of non–polar fraction of Cyperus rotundus Linn as anti-cancer candidate, J. Multidiscip. Appl. Nat. Sci., 5 (1), 56–73.
[16] Yogaswara. R., Pranowo, H.D., Prasetyo, N., and Pulung, M.L., 2025, Investigation of new 4-benzyloxy-2-trichloromethylquinazoline derivatives as Plasmodium falciparum dihydrofolate reductase-thymidylate synthase inhibitors: QSAR, ADME, drug-likeness, toxicity, molecular docking and molecular dynamics simulation, J. Multidiscip. Appl. Nat. Sci., 5 (2), 456–486.
[17] Fitria, A., Kurniawan, Y.S., Ananto, A.D., Jumina, J., Sholikhah, E.N., and Pranowo, H.D., 2025, Allyl-modified of calix[4]resorcinarene derivatives for HER2 inhibition agents: An in silico study, J. Multidiscip. Appl. Nat. Sci., 5 (2), 352–369.
[18] Ishak, J., Unsunnidhal, L., Martien, R., and Kusumawati, A., 2019, In vitro evaluation of chitosan–DNA plasmid complex encoding Jembrana disease virus Env-TM protein as a vaccine candidate, J. Vet. Res., 63 (1), 7–16.
[19] Melchjorsen, J., 2013, Learning from the messengers: Innate sensing of viruses and cytokine regulation of immunity – Clues for treatments and vaccines, Viruses, 5 (2), 470–527.
[20] Fisch, A., Reynisson, B., Benedictus, L., Nicastri, A., Vasoya, D., Morrison, I., Buus, S., Ferreira, B.R., Kinney Ferreira de Miranda Santos, I., Ternette, N., Connelley, T., and Nielsen, M., 2021, Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes, J. Immunol., 206 (10), 2489–2497.
[21] Yu, M., Zhu, Y., Li, Y., Chen, Z., Li, Z., Wang, J., Li, Z., Zhang, F., and Ding, J., 2022, Design of a recombinant multivalent epitope vaccine based on SARS-CoV-2 and its variants in immunoinformatics approaches, Front. Immunol., 13, 884433.
[22] Mazumder, L., Hasan, M.R., Fatema, K., Begum, S., Azad, A.K., and Islam, M.A., 2023, Identification of B and T cell epitopes to design an epitope-based peptide vaccine against the cell surface binding protein of monkeypox virus: An immunoinformatics study, J. Immunol. Res., 2023 (1), 2274415.
[23] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S.A.A., Ballard, A.J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A.W., Kavukcuoglu, K., Kohli, P., and Hassabis, D., 2021, Highly accurate protein structure prediction with AlphaFold, Nature, 596 (7873), 583–589.
[24] Heo, L., Park, H., and Seok, C., 2013, GalaxyRefine: Protein structure refinement driven by side-chain repacking, Nucleic Acids Res., 41 (W1), W384–W388.
[25] Dym, O., Eisenberg, D., and Yeates, T.O., 2012, ERRAT, Int. Tables Crystallogr., 21, 678–679.
[26] Laskowski, R.A., Jabłońska, J., Pravda, L., Vařeková, R.S., and Thornton, J.M., 2018, PDBsum: Structural summaries of PDB entries, Protein Sci., 27 (1), 129–134.
[27] Gasteiger, E., Hoogland, C., Gattiker, A., Duvaud, S., Wilkins, M.R., Appel, R.D., and Bairoch, A., 2005, “Protein Identification and Analysis Tools on the ExPASy Server” in The Proteomics Protocols Handbook, Eds. Walker, J.M., Humana Press, NJ, US, 571–607.
[28] Kottarathil, A., Murugan, G., Rajkumar, D.S., Chandran, A.K., Elumalai, V., and Padmanaban, R., 2025, Designing multiepitope-based vaccine targeting immunogenic proteins of Streptococcus mutans using immunoinformatics to prevent caries, The Microbe, 7, 100320.
[29] Doytchinova, I.A., and Flower, D.R., 2007, VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines, BMC Bioinf., 8 (1), 4.
[30] Dimitrov, I., Bangov, I., Flower, D.R., and Doytchinova, I., 2014, AllerTOP v.2—A server for in silico prediction of allergens, J. Mol. Model., 20 (6), 2278.
[31] Wei, L., Ye, X., Sakurai, T., Mu, Z., and Wei, L., 2022, ToxIBTL: Prediction of peptide toxicity based on information bottleneck and transfer learning, Bioinformatics, 38 (6), 1514–1524.
[32] Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., Lepore, R., and Schwede, T., 2018, SWISS-MODEL: Homology modelling of protein structures and complexes, Nucleic Acids Res., 46 (W1), W296–W303.
[33] Singh, A., Copeland, M.M., Kundrotas, P.J., and Vakser, I.A., 2024, GRAMM web server for protein docking, Methods Mol. Biol., 2714, 101–112.
[34] Jendele, L., Krivak, R., Skoda, P., Novotny, M., and Hoksza, D., 2019, PrankWeb: A web server for ligand binding site prediction and visualization, Nucleic Acids Res., 47 (W1), W345–W349.
[35] Kuriata, A., Gierut, A.M., Oleniecki, T., Ciemny, M.P., Kolinski, A., Kurcinski, M., and Kmiecik, S., 2018, CABS-flex 2.0: A web server for fast simulations of flexibility of protein structures, Nucleic Acids Res., 46 (W1), W338–W343.
[36] Singh, N., and Li, W., 2019, Recent advances in coarse-grained models for biomolecules and their applications, Int. J. Mol. Sci., 20 (15), 3774.
[37] Grote, A., Hiller, K., Scheer, M., Münch, R., Nörtemann, B., Hempel, D.C., and Jahn, D., 2005, JCat: A novel tool to adapt codon usage of a target gene to its potential expression host, Nucleic Acids Res., 33 (Suppl. 2), W526–W531.
[38] Rosano, G.L., Morales, E.S., and Ceccarelli, E.A., 2019, New tools for recombinant protein production in Escherichia coli: A 5-year update, Protein Sci., 28 (8), 1412–1422.
[39] Samad, A., Ahammad, F., Nain, Z., Alam, R., Imon, R.R., Hasan, M., and Rahman, M.S., 2022, Designing a multiepitope vaccine against SARS-CoV-2: An immunoinformatics approach, J. Biomol. Struct. Dyn., 40 (1), 14–30.
[40] Rachmat, J., 2010, Improved methods for production and characterisation of Jembrana disease virus proteins, Dissertation, Faculty of Health Science, Murdoch University, Perth, Australia.
[41] Wang, J., Alekseenko, A., Kozakov, D., and Miao Y., 2019, Improved modeling of peptide-protein binding through global docking and accelerated molecular dynamics simulations, Front. Mol. Biosci., 6, 112.
[42] Jespersen, M., Peters, B., Nielsen, M., and Marcatili, P., 2017, BepiPred-2.0: Improving sequence-based B-cell epitope prediction using conformational epitopes, Nucleic Acids Res., 45 (W1), W24–W29.
[43] Abidi, A., 2018, Validation tools for predicted linear B-epitopes: Surface accessibility, Southeast Eur. J. Soft Comput., 7 (1), 49–53.
[44] Agarwal, V., Tiwari, A., and Varadwaj, P., 2022, Prediction of suitable T and B cell epitopes for eliciting immunogenic response against SARS-CoV-2 and its mutant, Network Model. Anal. Health Inf. Bioinf., 11 (1), 1.
[45] Mollazadeh, S., Bakhshesh, M., Keyvanfar, H., and Nikbakht Brujeni, G., 2022, Identification of cytotoxic T lymphocyte (CTL) epitope and design of an immunogenic multi-epitope of bovine ephemeral fever virus (BEFV) glycoprotein G for vaccine development, Res. Vet. Sci., 144, 18–26.
[46] Andreatta, M., Karosiene, E., Rasmussen, M., Stryhn, A., Buus, S., and Nielsen, M., 2015, Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification, Immunogenetics, 67 (11-12), 641–650.
[47] Díaz-Dinamarca, D.A., Salazar, M.L., Castillo, B.N., Manubens, A., Vasquez, A.E., Salazar, F., and Becker, M.I., 2022, Protein-based adjuvants for vaccines as immunomodulators of the innate and adaptive immune response: Current knowledge, challenges, and future opportunities, Pharmaceutics, 14 (8), 1671.
[48] Arai, T.Y., 2021, Design of helical linkers for fusion proteins and protein-based nanostructures, Methods Enzymol., 647, 209–230.
[49] Khatrawi, E.M., Ali, S.L., Ali, S.Y., Abduldayeva, A., and Alhegaili, A.S., 2025, Designing a multi-epitope vaccine targeting UPF0721 of meningitis-causing Salmonella enterica serovar Typhimurium strain L-4126 by utilizing immuno-informatics and in silico approaches, Mol. Syst. Des. Eng., 10 (7), 549–566.
[50] Barreto, A.S., de Franca, M.N.F., dos Reis, T.L.S., Silva, J.A.B.M., Dos Santos, P.L., de Oliveira, F.A., da Silva, A.M., Magalhaes, L.S., Secco, D.A., Andrade, M.A.F., Porto, L.C., Rosa, D.S., Cavalcante, R.C.M., Corrêa, C.B., Sidney, J., Sette, A., de Almeida, R.P., and Palatnik-de-Sousa, C.B., 2025, Design and development of highly conserved, HLA-promiscuous T cell multiepitope vaccines against human visceral leishmaniasis, Front. Immunol., 16, 1540537.
[51] Ayyagari, V.S., Venkateswarulu, T.C., Karlapudi, A.P., and Srirama, K., 2022, Design of a multi-epitope-based vaccine targeting M-protein of SARS-CoV2: An immunoinformatics approach, J. Biomol. Struct. Dyn., 40 (7), 2963–2977.
[52] Gharazi, H., Doosti, A., and Abdizadeh, R., 2025, Brucellosis novel multi-epitope vaccine design based on in silico analysis focusing on Brucella abortus, BMC Immunol., 26 (1), 46.
[53] Pandey, R.K., Ojha, R., Aathmanathan, V.S., Krishnan, M., and Prajapati, V.K., 2018, Immunoinformatics approaches to design a novel multi-epitope subunit vaccine against HIV infection, Vaccine, 36 (17), 2262–2272.
[54] Grosvirt-Dramen, A., Urbach, Z.J., Hurst, P.J., Kwok, C.E., Patterson, J.P., Wang, F., and Hochbaum, A.I., 2025, Hierarchical assembly of conductive fibers from coiled-coil peptide building blocks, ACS Nano, 19 (10), 10162–10172.
[55] Sakuma, K., Kobayashi, N., Sugiki, T., Nagashima, T., Fujiwara, T., Suzuki, K., Kobayashi, N., Murata, T., Kosugi, T., Tatsumi-Koga, R., and Koga, N., 2024, Design of complicated all-α protein structures, Nat. Struct. Mol. Biol., 31 (2), 275–282.
[56] Thu, T.T.M., Co, N.T., Tu, L.A., and Li, M.S, 2019, Aggregation rate of amyloid beta peptide is controlled by beta-content in monomeric state, J. Chem. Phys., 150 (22), 225101.
[57] MacRaild, C.A., Seow, J., Das, S.C., and Norton, R.S., 2018, Disordered epitopes as peptide vaccines, Pept. Sci., 110 (3), e24067.
[58] Kakakhel, S., Ahmad, A., Mahdi, W.A., Alshehri, S., Aiman, S., Begum, S., Shams, S., Kamal, M., Imran, M., Shakeel, F., and Khan, A., 2023, Annotation of potential vaccine targets and designing of mRNA-based multiepitope vaccine against lumpy skin disease virus via reverse vaccinology and agent-based modeling, Bioengineering, 10 (4), 430.
[59] Paul, S.K., Saddam, M., Rahaman, K.A., Choi, J.G., Lee, S.S., and Hasan, M., 2022, Molecular modeling, molecular dynamics simulation, and essential dynamics analysis of grancalcin: An upregulated biomarker in experimental autoimmune encephalomyelitis mice, Heliyon, 8 (10), e11232.
[60] Shabbir, M.A., Amin, A., Hasnain, A., Shakeel, A., and Gul, A., 2025, Immunoinformatics-driven design of a multiepitope vaccine against nipah virus: A promising approach for global health protection, J. Genet. Eng. Biotechnol., 23 (2), 100482.
[61] Mahafujul Alam, S.S., Mir, S.A., Samanta, A., Nayak, B., Ali, S., and Hoque, M., 2025, Immunoinformatics-based designing of a multiepitope cancer vaccine targeting programmed cell death ligand 1, Sci. Rep., 15 (1), 12420.
[62] Alnuqaydan, A.M., and Eisa, A.A., 2024, Targeting polyprotein to design potential multiepitope vaccine against Omsk hemorrhagic fever virus (OHFV) by evaluating allergenicity, antigenicity, and toxicity using immunoinformatic approaches, Biology, 13 (9), 738.
[63] Waidyasooriya, H.M., Hariyama, M., and Kasahara, K., 2017, An FPGA accelerator for molecular dynamics simulation using OpenCL, Int. J. Networked Distrib. Comput., 5 (1), 52–61.
[64] Wankowicz, S.A., de Oliveira, S.H., Hogan, D.W., van den Bedem, H., and Fraser, J.S., 2022, Ligand binding remodels protein side-chain conformational heterogeneity, eLife, 11, e74114.
[65] Shahraki, P.K., Kiani, R., Siavash, M., and Bemani, P., 2025, Design of a multiepitope vaccine against Staphylococcus aureus lukotoxin ED using in silico approaches, Sci. Rep., 15 (1), 14517.
[66] Zaytsev, K., Bogatyreva, N., and Fedorov, A., 2024, Link between individual codon frequencies and protein expression: Going beyond codon adaptation index, Int. J. Mol. Sci., 25 (21), 11622.
[67] Redwan, E.M., 2006, The optimal gene sequence for optimal protein expression in Escherichia coli: Principle requirements, Arab J. Biotechnol., 9 (3), 493–510.
[68] Yin, D., Bai, Q., Zhang, J., Xu, K., and Li, J., 2020, A novel recombinant multiepitope protein candidate for the diagnosis of brucellosis: A pilot study, J. Microbiol. Methods, 174, 105964.
[69] Rosalez-Mendoza, S., Rubio-Infante, N., Monreal-Escalante, E., Govea-Alonso, D.O., Garcia-Hernandez, A.L., Salazar-Gonzales, J.A., Gonzales-Ortega, O., Paz-Maldonado, L.M.T., and Moreno-Fierros, L., 2014, Chloroplast expression of an HIV envelope-derived multiepitope protein: Towards a multivalent plant-based vaccine, Plant Cell, Tissue Organ Cult., 116 (1), 111–123.
[70] Khan, S., Rizwan, M., Zeb, A., Eldeen, M.A., Hassan, S., Ur Rehman, A., Eid, R.A., Zaki, M.S.A., Albadrani, G.M., Altyar, A.E., Nouh, N.A.T., Abdel-Daim, M.M., and Ullah, A., 2022, Identification of a potential vaccine against Treponema pallidum using subtractive proteomics and reverse-vaccinology approaches, Vaccines, 11 (1), 72.
[71] Nogimori, T., Suzuki, K., Masuta, Y., Washizaki, A., Yagoto, M., Ikeda, M., Katayama, Y., Kanda, H., Takada, M., Minami, S., Kobayashi, T., Takahama, S., Yoshioka, Y., and Yamamoto, T., 2023, Functional changes in cytotoxic CD8+ T-cell cross-reactivity against the SARS-CoV-2 Omicron variant after mRNA vaccination, Front. Immunol., 13, 1081047.
[72] Bhat, P., Leggatt, G., Waterhouse, N., and Frazer, I.H., 2017, Interferon-γ derived from cytotoxic lymphocytes directly enhances their motility and cytotoxicity, Cell Death Dis., 8 (6), e2836.
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