Computational Design of a Multi-Epitope Vaccine Against Nipah Virus: Bridging Immunoinformatics and Immune Protection
DOI:
https://doi.org/10.53560/PPASB(62-4)1116Keywords:
Nipah Virus, Multi-epitope Vaccine (MEV), Fusion Protein, Molecular Docking, Immunogenicity, VaccineAbstract
Nipah virus (NiV) is a highly lethal zoonotic paramyxovirus with no licensed vaccines or targeted antiviral therapies, posing a serious global health threat. Recurrent outbreaks in South and Southeast Asia highlight the critical need for efficacious and broadly protective vaccine strategies. In this research, an immunoinformatics-based approach was utilized to construct a multi-epitope vaccine (MEV) targeting the highly conserved NiV fusion protein (NCBI ID: AAY43915.1). The protein exhibited high antigenicity, non-allergenic potential, and favorable physicochemical properties. Cytotoxic T-lymphocyte (CTL), Helper T-lymphocyte (HTL), and B-cell epitopes were predicted and rigorously screened for immunogenicity, non-toxicity, and sequence conservancy, resulting in the selection of epitopes with over 90% identity across Bangladeshi and Malaysian NiV strains. Population coverage analysis confirmed the broad applicability of Human Leukocyte Antigen (HLA), particularly in endemic regions. The finalized MEV construct, incorporating appropriate linkers and a 50S ribosomal protein adjuvant, showed structural stability following modelling, refinement, and validation. Molecular docking revealed strong binding affinity with TLR3 and TLR4, Computational immune simulations predicted robust adaptive immune responses, and codon optimization, along with in silico cloning, confirmed favorable expression in E. coli. Although these findings are supported by computational analyses and should be validated experimentally, the proposed MEV demonstrates strong cross-protective and immunogenic potential, offering an encouraging platform for the design of a pan-strain NiV vaccine.
References
1. V. Sharma, S. Kaushik, R. Kumar, J.P. Yadav, and S. Kaushik. Emerging trends of Nipah virus: Reviews in Medical Virology 29(1): e2010 (2019). https://doi.org/10.1002/rmv.2010
2. S. Gazal, N. Sharma, M. Tikoo, D. Shikha, G.A. Badroo, M. Rashid, and S.J. Lee. Nipah and Hendra viruses: deadly zoonotic paramyxoviruses with the potential to cause the next pandemic. Pathogens 11(12): 1419 (2022). https://doi.org/10.3390/pathogens11121419
3. N. Sharif, N. Sharif, A. Khan, and S.K. Dey. Tackling the outbreak of nipah virus in Bangladesh amidst COVID‐19: A potential threat to public health and actionable measures. Health Science Reports 7(4): e2010 (2024). https://doi.org/10.1002/hsr2.2010
4. S. Kim, H. Kang, L. Skrip, S. Sahastrabuddhe, A. Islam, S.M. Jung, J.F. Vesga, A. Endo, W.J. Edmunds, and K. Abbas. Progress and challenges in Nipah vaccine development and licensure for epidemic preparedness and response. Expert Review of Vaccines (2025). https://doi.org/10.1080/14760584.2025.2476523
5. F. Waheed, A.S. Khan, and U. Nisa. Nipah virus; an overview and potential for outbreak in Pakistan. JPMA The Journal of the Pakistan Medical Association 74(12): 2214-2215 (2024). https://doi.org/10.47391/jpma.20661
6. T.P. Monath, R. Nichols, F. Feldmann, A. Griffin, E. Haddock, J. Callison, K. Meade-White, A. Okumura, J. Lovaglio, and P.W. Hanley. Immunological correlates of protection afforded by PHV02 live, attenuated recombinant vesicular stomatitis virus vector vaccine against Nipah virus disease. Frontiers in Immunology 14: 1216225 (2023). https://doi.org/10.3389/fimmu.2023.1216225
7. B. Tigabu, L. Rasmussen, E.L. White, N. Tower, M. Saeed, A. Bukreyev, B. Rockx, J.W. LeDuc, and J.W. Noah. A BSL-4 high-throughput screen identifies sulfonamide inhibitors of Nipah virus. Assay and Drug Development Technologies 12(3): 155-161 (2014). https://doi.org/10.1089/adt.2013.567
8. F.H. Tan, A. Sukri, N. Idris, K.C. Ong, J.P. Schee, C.T. Tan, S.H. Tan, K.T. Wong, L.P. Wong, and K.K. Tee. A systematic review on Nipah virus: global molecular epidemiology and medical countermeasures development. Virus Evolution 10(1): veae048 (2024). https://doi.org/10.1093/ve/veae048
9. B. Kaur, A. Karnwal, A. Bansal, and T. Malik. An immunoinformatic‐based In silico identification on the creation of a multiepitope‐based vaccination against the Nipah virus. BioMed Research International 2024(1): 4066641 (2024). https://doi.org/10.1186/s43141-020-00041-x
10. M.T.U. Qamar, A. Rehman, K. Tusleem, U.A. Ashfaq, M. Qasim, X. Zhu, I. Fatima, F. Shahid, and L.L. Chen. Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches. PLOS One 15(12): e0244176 (2020). https://doi.org/10.1371/journal.pone.0244176
11. P.K. Yadav and M. Mishra. Computational epitope prediction and docking studies of glycoprotein-G in Nipah virus. International Journal of Bioinformatics and Biological Science 1(1): 55-61 (2013). ndpublisher.in/admin/issues/biov1n1f.pdf
12. M. Shahab, M.W. Iqbal, A. Ahmad, F.M. Alshabrmi, D.Q. Wei, A. Khan, and G. Zheng. Immunoinformatics-driven in silico vaccine design for Nipah virus (NPV): integrating machine learning and computational epitope prediction. Computers in Biology and Medicine 170: 108056 (2024). https://doi.org/10.1016/j.compbiomed.2024.108056
13. R. Saha and B.V. Prasad. In silico approach for designing of a multi-epitope based vaccine against novel Coronavirus (SARS-COV-2). BioRxiv 2020.03. 31.017459 (2020). https://doi.org/10.1101/2020.03.31.017459
14. K.T. Wong, W.J. Shieh, S. Kumar, K. Norain, W. Abdullah, J. Guarner, C.S. Goldsmith, K.B. Chua, S.K. Lam, and C.T. Tan. Nipah virus infection: pathology and pathogenesis of an emerging paramyxoviral zoonosis. The American Journal of Pathology 161(6): 2153-2167 (2002). https://doi.org/10.1016/S0002-9440(10)64493-8
15. A. Sette, B. Livingston, D. McKinney, E. Appella, J. Fikes, J. Sidney, M. Newman, and R. Chesnut. The development of multi-epitope vaccines: epitope identification, vaccine design and clinical evaluation. Biologicals 29(3-4): 271-276 (2001). https://doi.org/10.1006/biol.2001.0297
16. K. Srivastava, and V. Srivastava. Prediction of conformational and linear B-cell epitopes on envelop protein of zika virus using immunoinformatics approach. International Journal of Peptide Research and Therapeutics 29(1): 17 (2023). https://doi.org/10.1007/s10989-022-10486-y
17. G. Anandhan, Y.B. Narkhede, M. Mohan, and P. Paramasivam. Immunoinformatics aided approach for predicting potent cytotoxic T cell epitopes of respiratory syncytial virus. Journal of Biomolecular Structure and Dynamics 41(21): 12093-12105 (2023). https://doi.org/10.1080/07391102.2023.2191136
18. Medha, P. Bhatt, Priyanka, M. Sharma, and S. Sharma. Prediction and identification of T cell epitopes of COVID-19 with balanced cytokine response for the development of peptide based vaccines. In Silico Pharmacology 9(1): 40 (2021). https://doi.org/10.1007/s40203-021-00098-7
19. S. Kumar, O. Nath, S. Govil, and A. Pathak. Computational 3D structure prediction, evaluation and analysis of pyruvate dehydrogenase an effective target for filarial infection by Brugia pahangi using homology modeling approach. International Journal of Pharmaceutical Sciences and Drug Research 6(2): 120-123 (2014). http://www.ijpsdr.com/pdf/vol6-issue2/7.pdf
20. S. Ahmad, F.M. Demneh, B. Rehman, T.N. Almanaa, N. Akhtar, H. Pazoki-Toroudi, A. Shojaeian, M. Ghatrehsamani, and S. Sanami. In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches. International Journal of Biological Macromolecules 267: 131517 (2024). https://doi.org/10.1016/j.ijbiomac.2024.131517
21. Z. Du, H. Su, W. Wang, L. Ye, H. Wei, Z. Peng, I. Anishchenko, D. Baker, and J. Yang. The trRosetta server for fast and accurate protein structure prediction. Nature Protocols 16(12): 5634-5651 (2021). https://doi.org/10.1038/s41596-021-00628-9
22. S.R. Mahapatra, J. Dey, T. Kaur, R. Sarangi, A.A. Bajoria, G.S. Kushwaha, N. Misra, and M. Suar. Immunoinformatics and molecular docking studies reveal a novel multi-epitope peptide vaccine against pneumonia infection. Vaccine 39(42): 6221-6237 (2021). https://doi.org/10.1016/j.vaccine.2021.09.025
23. D.B. Craig and A.A. Dombkowski. Disulfide by Design 2.0: a web-based tool for disulfide engineering in proteins. BMC Bioinformatics 14: 346(2013). https://doi.org/10.1186/1471-2105-14-346
24. S. Zaib, N. Rana, N. Hussain, H. Alrbyawi, A.A. Dera, I. Khan, M. Khalid, A. Khan, and A. Al-Harrasi. Designing multi-epitope monkeypox virus-specific vaccine using immunoinformatics approach. Journal of Infection and Public Health 16(1): 107-116 (2023). https://doi.org/10.1016/j.jiph.2022.11.033
25. S. Raju, D. Sahoo, and V.K. Bhari. In silico design of multi-epitope vaccine against Nipah virus using immunoinformatics approach. Journal of Pure & Applied Microbiology 15(1): 212-231(2021). https://doi.org/10.22207/JPAM.15.1.16
26. M.A. Soltan, M.A. Eldeen, N. Elbassiouny, I. Mohamed, D.A. El-Damasy, E. Fayad, O.A.A. Ali, N. Raafat, R.A. Eid, and A.A. Al-Karmalawy. Proteome-based approach defines candidates for designing a multitope vaccine against the Nipah virus. International Journal of Molecular Sciences 22(17): 9330 (2021). https://doi.org/10.3390/ijms22179330
27. M. Lu, Y. Yao, H. Liu, X. Zhang, X. Li, Y. Liu, Y. Peng, T. Chen, Y. Sun, and G. Gao. Vaccines based on the fusion protein consensus sequence protect Syrian hamsters from Nipah virus infection. JCI Insight 8(23): e175461 (2023). https://doi.org/10.1172/jci.insight.175461
28. D. Klingelhöfer, M. Braun, C.A. Naser, D. Brüggmann, and D.A. Groneberg. Emerging Nipah virus with pandemic potential and high mortality rates: is the scientific community learning from former pandemics? Reviews in Medical Virology 35(2): e70028 (2025). https://doi.org/10.1002/rmv.70028
29. P. Majee, N. Jain, and A. Kumar. Designing of a multi-epitope vaccine candidate against Nipah virus by in silico approach: a putative prophylactic solution for the deadly virus. Journal of Biomolecular Structure and Dynamics 39(4): 1461-80 (2021). https://doi.org/10.1080/07391102.2020.1734088
30. A.A. Mohammed, S.W. Shantier, M.I. Mustafa, H.K. Osman, H.E. Elmansi, I.A.A. Osman, R.A. Mohammed, F.A. Abdelrhman, M.E. Elnnewery, and E.M. Yousif. Epitope‐based peptide vaccine against glycoprotein G of Nipah henipavirus using immunoinformatics approaches. Journal of Immunology Research 2020(1): 2567957 (2020). https://doi.org/10.1155/2020/2567957
31. M.A. Shabbir, A. Amin, A. Hasnain, A. Shakeel, and A. Gul. Immunoinformatics-driven design of a multi-epitope vaccine against nipah virus: a promising approach for global health protection. Journal of Genetic Engineering and Biotechnology 23(2): 100482 (2025). https://doi.org/10.1016/j.jgeb.2025.100482
32. R. Santhoshkumar and A. Yusuf. In silico structural modeling and analysis of physicochemical properties of curcumin synthase (CURS1, CURS2, and CURS3) proteins of Curcuma longa. Journal of Genetic Engineering and Biotechnology 18(1): 24 (2020). https://doi.org/10.1186/s43141-020-00041-x
33. M.H.U. Masum, A.A. Mahdeen, L. Barua, R. Parvin, H.P. Heema, and J. Ferdous. Developing a chimeric multiepitope vaccine against Nipah virus (NiV) through immunoinformatics, molecular docking and dynamic simulation approaches. Microbial Pathogenesis 197: 107098 (2024). https://doi.org/10.1016/j.micpath.2024.107098
34. A. Alibakhshi, A.A. Bahrami, E. Mohammadi, S. Ahangarzadeh, and M. Mobasheri. In silico design of a new multi-epitope vaccine candidate against SARS-CoV-2. Acta Virologica 67: 12481 (2024). https://doi.org/10.3389/av.2023.12481
35. N. Rapin, O. Lund, M. Bernaschi, and F. Castiglione. Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system. PLOS One 5(4): e9862 (2010). https://doi.org/10.1371/journal.pone.0009862
36. R. Khandia, S. Singhal, U. Kumar, A. Ansari, R. Tiwari, K. Dhama, J. Das, A. Munjal, and R.K. Singh. Analysis of Nipah virus codon usage and adaptation to hosts. Frontiers in Microbiology 10: 886 (2019). https://doi.org/10.3389/fmicb.2019.00886
37. K. Li, S. Yan, N. Wang, W. He, H. Guan, C. He, Z. Wang, M. Lu, W. He, and R. Ye. Emergence and adaptive evolution of Nipah virus. Transboundary and Emerging Diseases 67(1): 121-32 (2020). https://doi.org/10.1111/tbed.13330
38. A. Kumar, G. Misra, S. Mohandas, and P.D. Yadav. Multi-epitope vaccine design using in silico analysis of glycoprotein and nucleocapsid of Nipah virus. PLOS One 19(5): e0300507 (2024). https://doi.org/10.1371/journal.pone.0300507
39. E.C. Banico, E.M.J.S. Sira, L.E. Fajardo, A.N.G. Dulay, N.M.O. Odchimar, A.M. Simbulan, and F.L. Orosco. Advancing one health vaccination: in silico design and evaluation of a multi-epitope subunit vaccine against Nipah virus for cross-species immunization using immunoinformatics and molecular modeling. PLOS One 19(9): e0310703 (2024). https://doi.org/10.1371/journal.pone.0310703
40. A. Albutti. An integrated multi-pronged reverse vaccinology and biophysical approaches for identification of potential vaccine candidates against Nipah virus. Saudi Pharmaceutical Journal 31(12): 101826 (2023). https://doi.org/10.1016/j.jsps.2023.101826
41. S. Sharma, P.D. Yadav, and S. Cherian. Comprehensive immunoinformatics and bioinformatics strategies for designing a multi-epitope-based vaccine targeting structural proteins of Nipah virus. Frontiers in Immunology 16: 1535322 (2025). https://doi.org/10.3389/fimmu.2025.1535322
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Upon acceptance of an article, its copyright will be assigned to the Pakistan Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons Attribution (CC BY). Allows users to: copy the article and distribute; abstracts, create extracts, and other revised versions, adaptations or derivative works of or from an article (such as a translation); include in a collective work (such as an anthology); and text or data mine the article. These uses are permitted even for commercial purposes, provided the user: includes a link to the license; indicates if changes were made; gives appropriate credit to the author(s) (with a link to the formal publication through the relevant DOI); and does not represent the author(s) as endorsing the adaptation of the article or modify the article in such a way as to damage the authors' honor or reputation.
