Reverse Vaccinology Analysis of B-cell Epitope against Nipah Virus using Fusion Protein
DOI:
https://doi.org/10.35516/jjps.v16i3.1602Keywords:
Fusion protein, Nipah virus, reverse vaccinology, immunoinformaticAbstract
Nipah virus (NiV) is an RNA virus, a pathogenic paramyxovirus that causes nonlethal respiratory illness in pigs. It was originally reported in Malaysia in 1998. NiV is considered a potential outbreak threat because it is zoonotic. However, no vaccines or antiviral drugs have been found against NiV. Therefore, the main objective is to develop effective vaccines by characterizing the fusion protein of NiV. We used a reference sequence retrieved from the National Center for Biotechnology Information (NCBI), then 3D modeled it to obtain the conserved region of the fusion protein. The interaction between the conserved region and B-cell receptors has been evaluated through a molecular docking approach. The B-cell epitope was identified using the Immune Epitope Database (IEDB) web server. As a result, we recommend Pep_D FANCISVTCQCQ as an epitope-based peptide vaccine candidate against Nipah virus. Pep D is highly immunogenic and does not cause autoimmune reactions. Pep D has the lowest binding energy for BCR molecular complexes, which can activate the transduction signal and direct B-cell immune response. However, further studies are required for confirmation (in vitro and in vivo).
References
Sakib M. S., Islam Md. R., Hasan A. K. M. M., and Nabi A. H. M. N., Prediction of Epitope-Based Peptides for the Utility of Vaccine Development from Fusion and Glycoprotein of Nipah Virus Using In Silico Approach, Adv. Bioinforma. 2014; 1–17. doi: 10.1155/2014/402492. DOI: https://doi.org/10.1155/2014/402492
Aditi and Shariff M., Nipah virus infection: A review, Epidemiol. Infect. 2019; 147: e95. doi: 10.1017/S0950268819000086. DOI: https://doi.org/10.1017/S0950268819000086
Ambat A. S. et al., Nipah virus: A review on epidemiological characteristics and outbreaks to inform public health decision making, J. Infect. Public Health. 2019; 12(5): 634–639. doi: 10.1016/j.jiph.2019.02.013. DOI: https://doi.org/10.1016/j.jiph.2019.02.013
Gupta G. B. M., Ahmed K. K. M., and Gupta R., Nipah Virus Research: A Scientometric Assessment of Global Publications Output during 1999-2018, Int. J. Med. Public Health. 2018; 8(2): 48–55. doi: 10.5530/ijmedph.2018.2.11. DOI: https://doi.org/10.5530/ijmedph.2018.2.11
Yoneda M. et al., Recombinant Measles Virus Vaccine Expressing the Nipah Virus Glycoprotein Protects against Lethal Nipah Virus Challenge. PLoS ONE. 2013: 8(3): e58414. doi: 10.1371/journal.pone.0058414. DOI: https://doi.org/10.1371/journal.pone.0058414
Database resources of the National Center for Biotechnology Information, Nucleic Acids Res. 2016. 44(D1): D7–D19. doi: 10.1093/nar/gkv1290. DOI: https://doi.org/10.1093/nar/gkv1290
Schwede T., SWISS-MODEL: an automated protein homology-modeling server, Nucleic Acids Res. 2003; 31(13): 3381–3385. doi: 10.1093/nar/gkg520. DOI: https://doi.org/10.1093/nar/gkg520
Vita R. et al., The Immune Epitope Database (IEDB): 2018 update, Nucleic Acids Res. 2019; 47(D1): D339–D343. doi: 10.1093/nar/gky1006. DOI: https://doi.org/10.1093/nar/gky1006
Doytchinova I. A. and Flower D. R., VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines, BMC Bioinformatics. 2007; 8(1): 4. doi: 10.1186/1471-2105-8-4. DOI: https://doi.org/10.1186/1471-2105-8-4
Gupta S. et al., In Silico Approach for Predicting Toxicity of Peptides and Proteins, PLoS ONE. 2013; 8(9): e73957. doi: 10.1371/journal.pone.0073957. DOI: https://doi.org/10.1371/journal.pone.0073957
Dimitrov I., Flower D. R., and Doytchinova I., AllerTOP - a server for in silico prediction of allergens, BMC Bioinformatics, 2013; (14)6: S4. doi: 10.1186/1471-2105-14-S6-S4. DOI: https://doi.org/10.1186/1471-2105-14-S6-S4
Berman H. M. et al., The Protein Data Bank, Nucleic Acids Res. 2000; 28(1): 235-242. doi: 10.1093/nar/28.1.235. DOI: https://doi.org/10.1093/nar/28.1.235
Lamiable A., Thévenet P., Rey J., Vavrusa M., Derreumaux P., and Tufféry P., PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex, Nucleic Acids Res. 2016; 44. doi: 10.1093/nar/gkw329. DOI: https://doi.org/10.1093/nar/gkw329
Schneidman-Duhovny D., Inbar Y., Nussinov R., and Wolfson H. J., PatchDock and SymmDock: servers for rigid and symmetric docking, Nucleic Acids Res., 2005; 33: W363–W367. doi: 10.1093/nar/gki481. DOI: https://doi.org/10.1093/nar/gki481
Meng X.-Y., Zhang H.-X., Mezei M., and Cui M., Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery, Curr. Comput. Aided-Drug Des. 2011; 7(2): 146-157. doi: 10.2174/157340911795677602. DOI: https://doi.org/10.2174/157340911795677602
Yuan S., Chan H. C. S., and Hu Z., Using PyMOL as a platform for computational drug design, WIREs Comput. Mol. Sci. 2017; 7(2). doi: 10.1002/wcms.1298. DOI: https://doi.org/10.1002/wcms.1298
Himmah K., Polytope Prediction for Dengue Vaccine Candidate Based on Conserved Envelope Glycoprotein of Four Serotypes of Dengue Virus and Its Antigenicity, J. Pure Appl. Chem. Res. 2016; 5(2): 101–107. doi: 10.21776/ub.jpacr.2016.005.02.290. DOI: https://doi.org/10.21776/ub.jpacr.2016.005.02.290
Zahroh H., Ma’rup A., Tambunan U. S. F., and Parikesit A. A., Immunoinformatics Approach in Designing Epitope-based Vaccine against Meningitis-inducing Bacteria (Streptococcus pneumoniae, Neisseria meningitidis, and Haemophilus influenzae Type b), Drug Target Insights. 2016; 10: DTI.S38458. doi: 10.4137/DTI.S38458. DOI: https://doi.org/10.4137/DTI.S38458
Fu Y., Zhao J., and Chen Z., Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein, Comput. Math. Methods Med. 2018; 2018: 1-12. doi: 10.1155/2018/3502514. DOI: https://doi.org/10.1155/2018/3502514
Borra, S. S., and Kumar, D. M., A Comprehensive Review on Efficacy and Adverse Events Associated with Different Covid-19 Vaccines, Jordan Journal of Pharmaceutical Sciences. 2022; 15(2): 289–304. doi: 10.35516/jjps.v15i2.326. DOI: https://doi.org/10.35516/jjps.v15i2.326
Obeidat, M. S., Alyahya, L. A., Obeidat, E. S., Obeidat, A., and Mukattash, T. L. Safety Practices in Community Pharmacy during COVID-19 Pandemic in Jordan, Jordan Journal of Pharmaceutical Sciences. 2023; 16(1): 11-17. doi: 10.35516/jjps.v16i1.1031 DOI: https://doi.org/10.35516/jjps.v16i1.1031
Al Jomaa, E. E., Al Meslamani, A., and Abazid, H., A. Comparative Cross-Sectional Study- Knowledge, behavior and psychological change among Medical and Nonmedical Students in Jordan during COVID-19 pandemic, Jordan Journal of Pharmaceutical Sciences. 2022; 15(2): 204-213. doi: 10.35516/jjps.v15i2.320. DOI: https://doi.org/10.35516/jjps.v15i2.320
