Skip to content
2000
Volume 6, Issue 4
  • ISSN: 2666-7967
  • E-ISSN: 2666-7975

Abstract

Background

SARS-CoV-2, which causes COVID-19, resulted in a global pandemic, and there were millions of confirmed cases and deaths worldwide. The vaccines were developed and distributed to help control the spread of the virus. The numbers and information related to the COVID-19 pandemic have likely evolved. Therefore, rapid immunological epitope identification would be a useful screening technique for vaccine candidates.

Objective

The aim of this study is to anticipate the protective epitopes for vaccine development using bioinformatics methods and resources.

Methodology

The SARS-CoV-2 genome and protein sequences were retrieved. Furthermore, using the ABCpred server, sequential B-cell epitope analysis was carried out. The Ellipro algorithm was used to forecast discontinuous B-cell epitopes. Moreover, by utilising the NetCTL server, a sequential T-cell epitope analysis was carried out. Furthermore, the 3D structure of the peptide was created using the PEP-FOLD3 server, and the 3D structure of the HLA molecule was identified using the homology modelling tool. The molecular docking was performed by AutoDock Vina.

Results

There were 20 B-cell epitopes altogether, of which 11 are highly antigenic. After assessing the antigenicity and toxicity of each resultant epitope, it was determined that the epitope SVLYNLAPFFTFKCYG is highly antigenic. Then, out of the 6 T-cell epitopes we had found, “RSYSFRPTY” was chosen as the epitope most suited for further research. Consequently, 72.42% of the population is covered overall. The structure that was generated was refined and energy-minimized. RSYSFRPTY's binding affinity to the groove of HLA-B*15:01 was determined by docking study to be -7.5 kcal/mol. PyMOL's visualisation of the docking result for predicting binding sites.

Conclusion

The final B-cell and T-cell epitopes are “SVLYNLAPFFTFKCYG” and “RSYSFRPTY” in terms of antigenicity score and nonallergenic and nontoxic qualities. An study indicated that our hypothesised T-cell epitope “RSYSFRPTY” had a greater affinity for binding with its receptor, which might elicit an immune response against the omicron variant.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/covid/10.2174/0126667975298936240705064458
2024-07-15
2025-10-03

  • From This Site

    /content/journals/covid/10.2174/0126667975298936240705064458
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. LiY.D. ChiW.Y. SuJ.H. FerrallL. HungC.F. WuT.C. Coronavirus vaccine development: from SARS and MERS to COVID-19.J. Biomed. Sci.202027110410.1186/s12929‑020‑00695‑2 33341119
     [Google Scholar]
  2. SinghalT. A review of coronavirus disease-2019 (COVID-19).Indian J. Pediatr.202087428128610.1007/s12098‑020‑03263‑6 32166607
     [Google Scholar]
  3. HeX. HongW. PanX. LuG. WeiX. SARS-CoV-2 omicron variant: Characteristics and prevention.MedComm202124838845Available from: https://doi.org/ 10.1002/mco2.110
     [Google Scholar]
  4. ShaoW. ZhangW. FangX. YuD. WangX. Challenges of SARS-CoV-2 Omicron Variant and appropriate countermeasures.J. Microbiol. Immunol. Infect.202255338739410.1016/j.jmii.2022.03.007 35501267
     [Google Scholar]
  5. YangQ SyedAAS FahiraA ShiY Structural analysis of the SARS-CoV-2 omicron variant proteins.Research202120212021/976958610.34133/2021/9769586 35088054
     [Google Scholar]
  6. FanY. LiX. ZhangL. WanS. ZhangL. ZhouF. SARS-CoV-2 Omicron variant: recent progress and future perspectives.Signal Transduct. Target. Ther.20227114110.1038/s41392‑022‑00997‑x 35484110
     [Google Scholar]
  7. RedondoN. Zaldívar-LópezS. GarridoJ.J. MontoyaM. SARS-CoV-2 accessory proteins in viral pathogenesis: Knowns and unknowns.Front. Immunol.20211270826410.3389/fimmu.2021.708264 34305949
     [Google Scholar]
  8. MagazineN. ZhangT. WuY. McGeeM.C. VeggianiG. HuangW. Mutations and evolution of the SARS-CoV-2 spike protein.Viruses202214364010.3390/v14030640 35337047
     [Google Scholar]
  9. AgarwalaR. BarrettT. BeckJ. Database resources of the national center for biotechnology information.Nucleic Acids Res.201846D1D8D1310.1093/nar/gkx1095 29140470
     [Google Scholar]
  10. AbdiS.A.H. AliA. SayedS.F. Abutahir, Ali A, Alam P. Multi-epitope-based vaccine candidate for monkeypox: An In Silico approach.Vaccines2022109156410.3390/vaccines10091564 36146643
     [Google Scholar]
  11. ArtimoP. JonnalageddaM. ArnoldK. ExPASy: SIB bioinformatics resource portal.Nucleic Acids Res.201240W1W597-60310.1093/nar/gks400 22661580
     [Google Scholar]
  12. DuZ. SuH. WangW. The trRosetta server for fast and accurate protein structure prediction.Nat. Protoc.202116125634565110.1038/s41596‑021‑00628‑9 34759384
     [Google Scholar]
  13. ChenJ. LiuH. YangJ. ChouK.C. Prediction of linear B-cell epitopes using amino acid pair antigenicity scale.Amino Acids200733342342810.1007/s00726‑006‑0485‑9 17252308
     [Google Scholar]
  14. SunP. JuH. LiuZ. Bioinformatics resources and tools for conformational B-cell epitope prediction.Comput. Math. Methods Med.2013201311110.1155/2013/943636 23970944
     [Google Scholar]
  15. BibiS. UllahI. ZhuB. In silico analysis of epitope-based vaccine candidate against tuberculosis using reverse vaccinology.Sci. Rep.2021111124910.1038/s41598‑020‑80899‑6 33441913
     [Google Scholar]
  16. PonomarenkoJ. BuiH.H. LiW. ElliPro: A new structure-based tool for the prediction of antibody epitopes.BMC Bioinform.20089151410.1186/1471‑2105‑9‑514 19055730
     [Google Scholar]
  17. AhmadT.A. EweidaA.E. El-SayedL.H. T-cell epitope mapping for the design of powerful vaccines.Vaccine Rep.20166132210.1016/j.vacrep.2016.07.002
     [Google Scholar]
  18. MazumderL. HasanM.R. FatemaK. BegumS. AzadA.K. IslamM.A. 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.2023202311410.1155/2023/2274415 36874624
     [Google Scholar]
  19. LamiableA. ThévenetP. ReyJ. VavrusaM. DerreumauxP. TufféryP. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex.Nucleic Acids Res.201644W1W449-5410.1093/nar/gkw329 27131374
     [Google Scholar]
  20. BurleyS.K. BhikadiyaC. BiC. RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences.Nucleic Acids Res.202149D1D437D45110.1093/nar/gkaa1038 33211854
     [Google Scholar]
/content/journals/covid/10.2174/0126667975298936240705064458
Loading
Identification of Epitope Against Omicron Variant of SARS-CoV-2: In Silico Vaccine Design Approach
Coronaviruses 6, E150724231970 (2025); https://doi.org/10.2174/0126667975298936240705064458
/content/journals/covid/10.2174/0126667975298936240705064458
/content/journals/covid/10.2174/0126667975298936240705064458
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): B-cell epitopes; COVID-19; in silico; major histocompatibility complex; Omicron; T-cell epitopes

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test