Immunoinformatics Approach to Epitope-based Vaccine Design Against the SARS-CoV-2 in Bangladeshi Patients

Overview

Journal J Genet Eng Biotechnol

Specialty Biotechnology

Date 2022 Sep 20

PMID 36125645

Authors

Shahina Akter

Muhammad Shahab

Md Murshed Hasan Sarkar

Chandni Hayat

Tanjina Akhtar Banu

Barna Goswami

Iffat Jahan

Eshrar Osman

Mohammad Samir Uzzaman

Md Ahashan Habib

Aftab Ali Shaikh

Md Salim Khan

Affiliations

Soon will be listed here.

Abstract

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic which has brought a great challenge to public health. After the first emergence of novel coronavirus SARS-CoV-2 in the city of Wuhan, China, in December 2019. As of March 2020, SARS-CoV-2 was first reported in Bangladesh and since then the country has experienced a steady rise in infections, resulting in 13,355,191 cases and 29,024 deaths as of 27 February 2022. Bioinformatics techniques are used to predict B cell and T cell epitopes from the new SARS-CoV-2 spike glycoprotein in order to build a unique multiple epitope vaccine. The immunogenicity, antigenicity scores, and toxicity of these epitopes were evaluated and chosen based on their capacity to elicit an immune response.

Result: The best multi-epitope of the possible immunogenic property was created by combining epitopes. EAAAK, AAY, and GPGPG linkers were used to connect the epitopes. In several computer-based immune response analyses, this vaccine design was found to be efficient, as well as having high population coverage.

Conclusion: This research is entirely reliant on the development of epitope-based vaccines, and these in silico findings would represent a major step forward in the development of a vaccine that might eradicate SARS-CoV-2 in Bangladeshi patients.

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References

Chiu S, Chan K, Chu K, Kwan S, Guan Y, Poon L . Human coronavirus NL63 infection and other coronavirus infections in children hospitalized with acute respiratory disease in Hong Kong, China. Clin Infect Dis. 2005; 40(12):1721-9. PMC: 7107956. DOI: 10.1086/430301. View

Oany A, Emran A, Jyoti T . Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach. Drug Des Devel Ther. 2014; 8:1139-49. PMC: 4149408. DOI: 10.2147/DDDT.S67861. View

Zheng W, Zhang C, Li Y, Pearce R, Bell E, Zhang Y . Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Rep Methods. 2021; 1(3). PMC: 8336924. DOI: 10.1016/j.crmeth.2021.100014. View

Zhu N, Zhang D, Wang W, Li X, Yang B, Song J . A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020; 382(8):727-733. PMC: 7092803. DOI: 10.1056/NEJMoa2001017. View

Nielsen M, Lundegaard C, Worning P, Lauemoller S, Lamberth K, Buus S . Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003; 12(5):1007-17. PMC: 2323871. DOI: 10.1110/ps.0239403. View

Hoque M, Sarkar M, Rahman M, Akter S, Banu T, Goswami B . SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts. Sci Rep. 2021; 11(1):24042. PMC: 8674272. DOI: 10.1038/s41598-021-03245-4. View

Doytchinova I, Flower D . VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007; 8:4. PMC: 1780059. DOI: 10.1186/1471-2105-8-4. View

Bui H, Sidney J, Dinh K, Southwood S, Newman M, Sette A . Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics. 2006; 7:153. PMC: 1513259. DOI: 10.1186/1471-2105-7-153. View

Herman R, Song P, Thirumalaiswamysekhar A . Value of eight-amino-acid matches in predicting the allergenicity status of proteins: an empirical bioinformatic investigation. Clin Mol Allergy. 2009; 7:9. PMC: 2773230. DOI: 10.1186/1476-7961-7-9. View

10.

Grote A, Hiller K, Scheer M, Munch R, Nortemann B, Hempel D . JCat: a novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res. 2005; 33(Web Server issue):W526-31. PMC: 1160137. DOI: 10.1093/nar/gki376. View

11.

Dimitrov I, Bangov I, Flower D, Doytchinova I . AllerTOP v.2--a server for in silico prediction of allergens. J Mol Model. 2014; 20(6):2278. DOI: 10.1007/s00894-014-2278-5. View

12.

Ferre F, Clote P . DiANNA 1.1: an extension of the DiANNA web server for ternary cysteine classification. Nucleic Acids Res. 2006; 34(Web Server issue):W182-5. PMC: 1538812. DOI: 10.1093/nar/gkl189. View

13.

Sanami S, Zandi M, Pourhossein B, Mobini G, Safaei M, Abed A . Design of a multi-epitope vaccine against SARS-CoV-2 using immunoinformatics approach. Int J Biol Macromol. 2020; 164:871-883. PMC: 7362859. DOI: 10.1016/j.ijbiomac.2020.07.117. View

14.

Khan M, Zaman S, Chakraborty S, Chakravorty R, Alam M, Bhuiyan T . In silico predicted mycobacterial epitope elicits in vitro T-cell responses. Mol Immunol. 2014; 61(1):16-22. DOI: 10.1016/j.molimm.2014.04.009. View

15.

Rahaman M, Sarkar M, Rahman M, Islam M, Islam I, Saha O . Genomic characterization of the dominating Beta, V2 variant carrying vaccinated (Oxford-AstraZeneca) and nonvaccinated COVID-19 patient samples in Bangladesh: A metagenomics and whole-genome approach. J Med Virol. 2021; 94(4):1670-1688. DOI: 10.1002/jmv.27537. View

16.

Tahir Ul Qamar M, Saleem S, Ashfaq U, Bari A, Anwar F, Alqahtani S . Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: an immune-informatics study. J Transl Med. 2019; 17(1):362. PMC: 6839065. DOI: 10.1186/s12967-019-2116-8. View

17.

Chen H, Tang L, Yu X, Zhou J, Chang Y, Wu X . Bioinformatics analysis of epitope-based vaccine design against the novel SARS-CoV-2. Infect Dis Poverty. 2020; 9(1):88. PMC: 7395940. DOI: 10.1186/s40249-020-00713-3. View

18.

Khan S, Khan A, Rehman A, Ahmad I, Ullah S, Khan A . Immunoinformatics and structural vaccinology driven prediction of multi-epitope vaccine against Mayaro virus and validation through in-silico expression. Infect Genet Evol. 2019; 73:390-400. DOI: 10.1016/j.meegid.2019.06.006. View

19.

Buchan D, Jones D . The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res. 2019; 47(W1):W402-W407. PMC: 6602445. DOI: 10.1093/nar/gkz297. View

20.

Zhang L . Multi-epitope vaccines: a promising strategy against tumors and viral infections. Cell Mol Immunol. 2017; 15(2):182-184. PMC: 5811687. DOI: 10.1038/cmi.2017.92. View