Skip to main content

Advertisement

Log in

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

  • Original Article
  • Published:
VirusDisease Aims and scope Submit manuscript

Abstract

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Saha R, Ghosh P, Burra VP. Designing a next generation multi-epitope based peptide vaccine candidate against SARS-CoV-2 using computational approaches. Biotech. 2021;11:47. https://doi.org/10.1007/s13205-020-02574-x.

    Article  Google Scholar 

  2. Finco O, Rappuoli R, Smith KA. Designing vaccines for the twenty-first century society. 2014. www.frontiersin.org

  3. Enayatkhani M, Hasaniazad M, Faezi S, Gouklani H, Davoodian P, Ahmadi N, et al. Reverse vaccinology approach to design a novel multi-epitope vaccine candidate against COVID-19: an in silico study. J Biomol Struct Dyn. 2021;39:2857–72. https://doi.org/10.1080/07391102.2020.1756411.

    Article  CAS  PubMed  Google Scholar 

  4. Patronov A, Doytchinova I. T-cell epitope vaccine design by immunoinformatics. Open Biol. 2013. https://doi.org/10.1098/rsob.120139.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ahammad I, Lira SS. Designing a novel mRNA vaccine against SARS-CoV-2: an immunoinformatics approach. Int J Biol Macromol. 2020;162:820–37. https://doi.org/10.1016/j.ijbiomac.2020.06.213.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Noorimotlagh Z, Karami C, Mirzaee SA, Kaffashian M, Mami S, Azizi M. Immune and bioinformatics identification of T cell and B cell epitopes in the protein structure of SARS-CoV-2: a systematic review. Int Immunopharmacol. 2020;86:106738. https://doi.org/10.1016/j.intimp.2020.106738.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. COVID-19 vaccine tracker and landscape. https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines

  8. Dunn-Walters DK, Gray JJ, Chen K, Nielsen M, Dk M, Marcatili P, et al. Antibody specific B-cell epitope predictions: leveraging information from antibody-antigen protein complexes. Front Immunol. 2019;10:298.

    Article  Google Scholar 

  9. Blanco E, Ferrari M. Emerging nanotherapeutic strategies in breast cancer. Breast Church Livingstone. 2014;23:10–8.

    Google Scholar 

  10. Li W, Joshi MD, Singhania S, Ramsey KH, Murthy AK. Peptide vaccine: progress and challenges. Vaccines. 2014;2:515–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Qadir A, Riaz M, Saeed M, Shahzad-ul-Hussan S. Potential targets for therapeutic intervention and structure based vaccine design against Zika virus. Eur J Med Chem. 2018;156:444–60. https://doi.org/10.1016/j.ejmech.2018.07.014.

    Article  CAS  PubMed  Google Scholar 

  12. Bowden TA, Crispin M, Harvey DJ, Aricescu AR, Grimes JM, Jones EY, et al. Crystal structure and carbohydrate analysis of nipah virus attachment glycoprotein: a template for antiviral and vaccine design. J Virol. 2008;82:11628–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jabbar B, Rafique S, Salo-Ahen OMH, Ali A, Munir M, Idrees M, et al. Antigenic peptide prediction from E6 and E7 oncoproteins of HPV types 16 and 18 for therapeutic vaccine design using immunoinformatics and MD simulation analysis. Front Immunol. 2018;9:3000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ye ZW, Yuan S, Yuen KS, Fung SY, Chan CP, Jin DY. Zoonotic origins of human coronaviruses. Int J Biol Sci. 2020;16:1686–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Abdelmageed MI, Abdelmoneim AH, Mustafa MI, Elfadol NM, Murshed NS, Shantier SW, et al. Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach. BioMed Res Int. 2020. https://doi.org/10.1155/2020/2683286.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Crooke SN, Ovsyannikova IG, Kennedy RB, Poland GA. Immunoinformatic identification of B cell and T cell epitopes in the SARS-CoV-2 proteome. bioRxiv. 2020

  17. Gao H, Yao H, Yang S, Li L. From SARS to MERS: evidence and speculation. Front Med. 2016;10:377–82.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Greenbaum JA, Andersen PH, Blythe M, Bui HH, Cachau RE, Crowe J, et al. Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools. J Mol Recognit. 2007;20:75–82.

    Article  CAS  PubMed  Google Scholar 

  19. Behmard E, Soleymani B, Najafi A, Barzegari E. Immunoinformatic design of a COVID-19 subunit vaccine using entire structural immunogenic epitopes of SARS-CoV-2. Sci Rep. 2020;10:1–28.

    Article  Google Scholar 

  20. Yang Z, Bogdan P, Nazarian S. An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study. Sci Rep. 2021;11:1–21. https://doi.org/10.1038/s41598-021-81749-9.

    Article  CAS  Google Scholar 

  21. Rahman MS, Hoque MN, Islam MR, Akter S, Ul Alam ASMR, Siddique MA, et al. Epitope-based chimeric peptide vaccine design against S, M and e proteins of SARS-CoV-2, the etiologic agent of COVID-19 pandemic: an in silico approach. PeerJ. 2020;8:e9572.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gangaev A, Ketelaars SLC, Isaeva OI, Patiwael S, Dopler A, Hoefakker K, et al. Identification and characterization of a SARS-CoV-2 specific CD8+ T cell response with immunodominant features. Nat Commun. 2021;12:2593.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Creech CB, Walker SC, Samuels RJ. SARS-CoV-2 vaccines. JAMA. 2021;325:1318–20.

    Article  CAS  PubMed  Google Scholar 

  24. Rauta PR, Ashe S, Nayak D, Nayak B. In silico identification of outer membrane protein (Omp) and subunit vaccine design against pathogenic Vibrio cholerae. Comput Biol Chem. 2016;65:61–8. https://doi.org/10.1016/j.compbiolchem.2016.10.004.

    Article  CAS  PubMed  Google Scholar 

  25. Pedersen SF, Ho Y-C. SARS-CoV-2: a storm is raging. J Clin Invest. 2020;130:2202–5. https://doi.org/10.1172/JCI137647.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Macri C, Dumont C, Johnston AP, Mintern JD. Targeting dendritic cells: a promising strategy to improve vaccine effectiveness. Clin Transl Immunol. 2016;5:e66. https://doi.org/10.1038/cti.2016.6.

    Article  CAS  Google Scholar 

  27. Kreer C, Gruell H, Mora T, Walczak AM, Klein F. Exploiting B cell receptor analyses to inform on HIV-1 vaccination strategies. Vaccines. 2020;8:1–19.

    Article  Google Scholar 

  28. Shang J, Wan Y, Luo C, Ye G, Geng Q, Auerbach A, et al. Cell entry mechanisms of SARS-CoV-2. Proc Natl Acad Sci. 2020;117:11727–34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Meyers LM, Gutiérrez AH, Boyle CM, Terry F, McGonnigal BG, Salazar A, et al. Highly conserved, non-human-like, and cross-reactive SARS-CoV-2 T cell epitopes for COVID-19 vaccine design and validation. npj Vaccines. 2021;6:1–14. https://doi.org/10.1038/s41541-021-00331-6.

    Article  CAS  Google Scholar 

  30. Jurewicz MM, Stern LJ. Class II MHC antigen processing in immune tolerance and inflammation. Immunogenetics. 2019. https://doi.org/10.1007/s00251-018-1095-x.

    Article  PubMed  Google Scholar 

  31. Role of MHC Gene Products in Immune Regulation on JSTOR [Internet]. [cited 2021 Sep 3]. https://www.jstor.org/stable/1685781?seq=1#metadata_info_tab_contents

  32. Kar T, Narsaria U, Basak S, Deb D, Castiglione F, Mueller DM, et al. A candidate multi-epitope vaccine against SARS-CoV-2. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-67749-1.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Flower DR, Doytchinova I, Zaharieva N, Dimitrov I. Immunogenicity prediction by VaxiJen: a ten year overview. J Proteomics Bioinform. 2017;10:298–310.

    Article  Google Scholar 

  34. Saha S, Raghava GPS. Prediction methods for B-cell epitopes. Methods Mol Biol. 2007;409:387–94.

    Article  CAS  PubMed  Google Scholar 

  35. Paul S, Sidney J, Sette A, Peters B. TepiTool: a pipeline for computational prediction of T cell epitope candidates. Curr Protoc Immunol. 2016;2016:18.19.1-18.19.24.

    Google Scholar 

  36. Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 2018;154:394–406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Trolle T, McMurtrey CP, Sidney J, Bardet W, Osborn SC, Kaever T, et al. The length distribution of class I-restricted T cell epitopes is determined by both peptide supply and MHC allele-specific binding preference. J Immunol. 2016;196:1480–7.

    Article  CAS  PubMed  Google Scholar 

  38. Dhanda SK, Vaughan K, Schulten V, Grifoni A, Weiskopf D, Sidney J, et al. Development of a novel clustering tool for linear peptide sequences. Immunology. 2018;155:331–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dimitrov I, Flower DR, Doytchinova I. AllerTOP-a server for in silico prediction of allergens [Internet]. 2011. http://www.biomedcentral.com/1471-2105/14/S6/S4

  40. Bui HH, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinform. 2006;7:1–5.

    Article  Google Scholar 

  41. Wilkins MR, Gasteiger E, Bairoch A, Sanchez JC, Williams KL, Appel RD, et al. Protein identification and analysis tools in the ExPASy server. Methods Mol Biol. 1999;112:531–52.

    CAS  PubMed  Google Scholar 

  42. Geourjon C, Deléage G. Sopma: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics. 1995;11:681–4.

    Article  CAS  Google Scholar 

  43. Schrödinger at the 236th ACS National Meeting & Exposition, Philadelphia, PA, August 17–21 | Schrödinger [Internet]. https://www.schrodinger.com/conferences-meetings/schrodinger-236th-acs-national-meeting-exposition-philadelphia-pa-august-17-21

  44. Gupta S, Singh AK, Kushwaha P, Prajapati KS, Shuaib M, Senapati S, et al. Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies. https://www.tandfonline.com/action/journalInformation?journalCode=tbsd20

  45. Xue LC, Ao J, Rodrigues P, Kastritis PL, Bonvin AM, Vangone A. Structural bioinformatics PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. http://milou.science.uu.nl/services/PRODIGY.

  46. Drug allergy: an updated practice parameter. Ann Allergy Asthma Immunol [Internet]. Ann Allergy Asthma Immunol; 2010;105. https://pubmed.ncbi.nlm.nih.gov/20934625/

  47. Altmann DM, Boyton RJ, Beale R. Immunity to SARS-CoV-2 variants of concern. Science. 2021;371:1103–4.

    Article  CAS  PubMed  Google Scholar 

  48. Tran HN, Le GT, Nguyen DT, Juang RS, Rinklebe J, Bhatnagar A, et al. SARS-CoV-2 coronavirus in water and wastewater: a critical review about presence and concern. Environ Res. 2021;193:110265.

    Article  CAS  PubMed  Google Scholar 

  49. Enjuanes L, Zuñiga S, Castaño-Rodriguez C, Gutierrez-Alvarez J, Canton J, Sola I. Molecular basis of coronavirus virulence and vaccine development. Adv Virus Res Academic Press. 2016;96:245–86.

    Article  CAS  Google Scholar 

  50. Arab-Zozani M, Hassanipour S. Features and limitations of LitCovid hub for quick access to literature about COVID-19. Balkan Med J. 2020;37:231.

    PubMed  PubMed Central  Google Scholar 

  51. Ahmed SF, Quadeer AA, McKay MR. Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses. 2020;12:254.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell Cell Press. 2020;181:281-292.e6.

    CAS  Google Scholar 

  53. Wang J, Peng Y, Xu H, Cui Z, Williams RO. The COVID-19 vaccine race: challenges and opportunities in vaccine formulation. AAPS PharmSciTech. 2020;21:1–12. https://doi.org/10.1208/s12249-020-01744-7.

    Article  CAS  Google Scholar 

  54. Bhattacharya M, Sharma AR, Patra P, Ghosh P, Sharma G, Patra BC, et al. Development of epitope-based peptide vaccine against novel coronavirus 2019 (SARS-COV-2): immunoinformatics approach. J Med Virol. 2020;92:618–31. https://doi.org/10.1002/jmv.25736.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kharisma VD, Ansori ANM. Construction of epitope-based peptide vaccine against SARS-CoV-2: immunoinformatics study. J Pure Appl Microbiol. 2020;14:999–1005.

    Article  CAS  Google Scholar 

  56. Samad A, Ahammad F, Nain Z, Alam R, Imon RR, Hasan M, et al. Designing a multi-epitope vaccine against SARS-CoV-2: an immunoinformatics approach. J Biomol Struct Dyn. 2020;0:1–17. https://doi.org/10.1080/07391102.2020.1792347.

    Article  CAS  Google Scholar 

  57. Panda PK, Arul MN, Patel P, Verma SK, Luo W, Rubahn HG, et al. Structure-based drug designing and immunoinformatics approach for SARS-CoV-2. Sci Adv. 2020;6:1–15.

    Article  Google Scholar 

  58. Prachar M, Justesen S, Steen-Jensen DB, Thorgrimsen S, Jurgons E, Winther O, et al. Abstract PO-046: assessment of COVID-19 vaccine candidates: prediction and validation of 174 SARS-CoV-2 epitopes. 2020;PO-046-PO-046

  59. Sarkar B, Ullah MA, Johora FT, Taniya MA, Araf Y. Immunoinformatics-guided designing of epitope-based subunit vaccines against the SARS Coronavirus-2 (SARS-CoV-2). Immunobiology. 2020;225:151955. https://doi.org/10.1016/j.imbio.2020.151955.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Costa JG, Faccendini PL, Sferco SJ, Lagier CM, Marcipar IS. Evaluation and comparison of the ability of online available prediction programs to predict true linear B-cell epitopes. Sharjah: Bentham Science Publishers; 2013.

    Book  Google Scholar 

  61. Karosiene E, Lundegaard C, Lund O, Nielsen M. NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. www.cbs.dtu

  62. Peters B, Bulik S, Tampe R, van Endert PM, Holzhütter H-G. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol. 2003;171:1741–9.

    Article  CAS  PubMed  Google Scholar 

  63. Kiyotani K, Toyoshima Y, Nemoto K, Nakamura Y. Bioinformatic prediction of potential T cell epitopes for SARS-Cov-2. J Hum Genet. 2020;65:569–75. https://doi.org/10.1038/s10038-020-0771-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Poran A, Harjanto D, Malloy M, Rooney MS, Srinivasan L, Gaynor RB. Sequence-based prediction of vaccine targets for inducing T cell responses to SARS-CoV-2 utilizing the bioinformatics predictor RECON. bioRxiv. 2020;1–30

  65. Youngblood B, Hale JS, Kissick HT, Ahn E, Xu X, Wieland A, et al. Effector CD8 T cells dedifferentiate into long-lived memory cells. Nat. 2017;552:404–9.

    Article  CAS  Google Scholar 

  66. Guermonprez P, Valladeau J, Zitvogel L, Théry C, Amigorena S. Antigen presentation and T cell stimulation by dendritic cells. Annu Rev Immunol. 2002;20:621–67.

    Article  CAS  PubMed  Google Scholar 

  67. Hale JS, Youngblood B, Latner DR, Mohammed AUR, Ye L, Akondy RS, et al. Distinct memory CD4+ T cells with commitment to T follicular Helper- and T Helper 1-cell lineages are generated after acute viral infection. Immunity Cell Press. 2013;38:805–17.

    CAS  Google Scholar 

  68. Chen HZ, Tang LL, Yu XL, Zhou J, Chang YF, Wu X. Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2. Infect Dis Poverty. 2020;9:1–10.

    Article  CAS  Google Scholar 

  69. Singh A, Thakur M, Sharma LK, Chandra K. Designing a multi-epitope peptide based vaccine against SARS-CoV-2. Sci Rep. 2020;10:16219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Smit LAM, Heederik D, Doekes G, Blom C, Van Zweden I, Wouters IM. Exposure-response analysis of allergy and respiratory symptoms in endotoxin-exposed adults. Eur Respir J. 2008;31:1241–8.

    Article  CAS  PubMed  Google Scholar 

  71. Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. A sequence homology and bioinformatic approach can predict candidate targets for immune responses to SARS-CoV-2. Cell Host Microbe Cell Press. 2020;27:671-680.e2.

    Article  CAS  Google Scholar 

  72. Medzhitov R, Janeway CA. Innate immunity: impact on the adaptive immune response. Curr Opin Immunol. 1997;9:4–9.

    Article  CAS  PubMed  Google Scholar 

  73. Sever W, Garg VK, Avashthi H, Tiwari A, Jain A, Ramkete PW, et al. MFPPI-multi FASTA ProtParam interface. Open access. 2016;12:74.

    Google Scholar 

  74. Lee CH, Koohy H, Wilkinson K, de Palma R, Bonsack M, Riemer A. In silico identification of vaccine targets for 2019-nCoV. F1000Research. 2020;9:1–10.

    Article  Google Scholar 

  75. Kalyanaraman N. In silico prediction of potential vaccine candidates on capsid protein of human bocavirus 1. Mol Immunol Pergamon. 2018;93:193–205.

    Article  CAS  Google Scholar 

  76. Somvanshi P, Singh V, Seth PK. In Silico prediction of epitopes in virulence proteins of mycobacterium tuberculosis H37Rv for diagnostic and subunit vaccine design. J Proteomics Bioinform. 2008;01:143–53.

    Article  CAS  Google Scholar 

  77. Sanami S, Zandi M, Pourhossein B, Mobini GR, Safaei M, Abed A, et al. Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. Int J Biol Macromol. 2020;164:871–83. https://doi.org/10.1016/j.ijbiomac.2020.07.117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Joshi A, Joshi BC, Mannan MA, ul Kaushik V. Epitope based vaccine prediction for SARS-COV-2 by deploying immuno-informatics approach. Informatics Med Unlocked. 2020;19:100338. https://doi.org/10.1016/j.imu.2020.100338.

    Article  Google Scholar 

  79. Wang D, Mai J, Zhou W, Yu W, Zhan Y, Wang N, et al. Immunoinformatic analysis of T-and B-cell epitopes for SARS-CoV-2 vaccine design. Vaccines. 2020;8:1–15.

    Article  Google Scholar 

  80. Yuan M, Wu NC, Zhu X, Lee CCD, So RTY, Lv H, et al. A highly conserved cryptic epitope in the receptor binding domains of SARS-CoV-2 and SARS-CoV. Science (80-). 2020;368:630–3.

    Article  CAS  Google Scholar 

  81. Bhattacharya M et al. Therapeutic role of neutralizing antibody for the treatment against SARS-CoV-2 and its emerging variants: a clinical and pre-clinical perspective. 2022; Vaccines 10.10-1612

Download references

Acknowledgements

Author acknowledges Priyank Shukla’s contribution towards verifying and guiding through initial data analysis.

Funding

We acknowledge NIT Rourkela for supporting this research on COVID-19.

Author information

Authors and Affiliations

Authors

Contributions

SM has contributed towards literature survey, formal analysis, data curation and wrote the whole manuscript. SK has contributed to data analysis. SK and AKS helped in molecular docking study. BN has supervised and validated the entire manuscript and helped in editing the manuscript.

Corresponding author

Correspondence to Bismita Nayak.

Ethics declarations

Conflict of interest

Authors declare that there are no conflicts of 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 file 1 (DOCX 90 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohapatra, S., Kumar, S., Kumar, S. et al. Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines. VirusDis. 34, 456–482 (2023). https://doi.org/10.1007/s13337-023-00852-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13337-023-00852-9

Keywords