Generic placeholder image

Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

In-Silico Designing of a Multi-Epitope Vaccine against SARS-CoV2 and Studying the Interaction of the Vaccine with Alpha, Beta, Delta and Omicron Variants of Concern

Author(s): Aranya Pal, Nibedita Pyne and Santanu Paul*

Volume 20, Issue 1, 2023

Published on: 20 October, 2022

Article ID: e090922208713 Pages: 22

DOI: 10.2174/1570163819666220909114900

Price: $65

Abstract

Background: The sudden appearance of the SARS-CoV2 virus has almost changed the future of vaccine development. There have been many different approaches to vaccination; among them, computational vaccinology in the form of multi-epitope vaccines with excellent immunological properties and minimal contamination or other adverse reactions has emerged as a promising strategy with a lot of room for further study in this area.

Objective: Designing a multi-epitope vaccine from the spike protein of SARS-CoV2 based on immunoinformatics and in-silico techniques. Evaluating the binding affinity of the constructed vaccine against the major variants of concern (alpha, beta, delta, and omicron) using docking studies.

Methods: The potential antigenic, immunogenic, and non-allergic T-cell epitopes were thoroughly explored using IEDB, NetCTL1.2, and NetMHCII pan 3.2 servers. The best suitable linker was identified using the ExPASy Protparam tool and VERIFY 3D. The 3D model of the vaccine was developed by RaptorX and the model was validated using ERRAT, Z-score, and Ramachandran Plot. Docking studies of the vaccine with TLR-2, 3, 4, and 7 and alpha, beta, delta, and omicron variants were performed using HADDOCK 2.4.

Results: The vaccine construct showed good antigenic and immunogenic scores and was non-allergic as well. The model was capable of binding to all four selected Toll-like receptors. Docking scores with variants were also promising.

Conclusion: All the variants showed good binding ability with the vaccine construct. Interaction with the alpha variant was found to be the most intense, followed by delta, beta, and omicron.

Keywords: Epitopes, Multi-epitope vaccine, in-silico, Variants of concern, Docking

Graphical Abstract

[1]
Liu YC, Kuo RL, Shih SR. COVID-19: The first documented coronavirus pandemic in history. Biomed J 2020; 43(4): 328-33.
[http://dx.doi.org/10.1016/j.bj.2020.04.007] [PMID: 32387617]
[2]
Decaro N, Lorusso A. Novel human coronavirus (SARS-CoV-2): A lesson from animal coronaviruses. Vet Microbiol 2020; 244: 108693.
[http://dx.doi.org/10.1016/j.vetmic.2020.108693] [PMID: 32402329]
[3]
Cucinotta D, Vanelli M. WHO declares COVID-19 a pandemic. Acta Biomed 2020; 91(1): 157-60.
[PMID: 32191675]
[4]
Paul D, Pyne N, Paul S. Mutation profile of SARS-CoV-2 spike protein and identification of potential multiple epitopes within spike protein for vaccine development against SARS-CoV-2. Virusdisease 2021; 32(4): 703-26.
[http://dx.doi.org/10.1007/s13337-021-00747-7] [PMID: 34754886]
[5]
Aleem A, Akbar Samad AB, Slenker AK. Emerging variants of SARS-CoV-2 and novel therapeutics against coronavirus (COVID-19). In: Treasure Island (FL): StatPearls Publishing 2022.
[6]
Walls AC, Park YJ, Tortorici MA, Wall A, McGuire AT, Veesler D. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 2020; 181(2): 281-292.e6.
[http://dx.doi.org/10.1016/j.cell.2020.02.058] [PMID: 32155444]
[7]
Wrapp D, Wang N, Corbett KS, et al. Cryo-EM structure of the 2019 -nCoV spike in the prefusion conformation. bioRxiv 2020.
[http://dx.doi.org/10.1101/2020.02.11.944462]
[8]
Naqvi AAT, Fatima K, Mohammad T, et al. Insights into SARS-CoV-2 genome, structure, evolution, pathogenesis and therapies: Structural genomics approach. Biochim Biophys Acta Mol Basis Dis 2020; 1866(10): 165878.
[http://dx.doi.org/10.1016/j.bbadis.2020.165878] [PMID: 32544429]
[9]
Li W, Joshi M, Singhania S, Ramsey K, Murthy A. Peptide vaccine: Progress and challenges. Vaccines 2014; 2(3): 515-36.
[http://dx.doi.org/10.3390/vaccines2030515] [PMID: 26344743]
[10]
Sunil KA, Aryandra A. Epitope based vaccine designing- A mini review. J Vaccines Immunol 2020; 6: 38-41.
[11]
Peters B, Nielsen M, Sette A. T cell epitope predictions. Annu Rev Immunol 2020; 38: 123-45.
[12]
Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T- and B-cell epitope prediction. J Immunol Res 2017; 2017: 2680160.
[13]
Moutaftsi M, Peters B, Pasquetto V, et al. A consensus epitope prediction of murine T CD8 + -cell responses to vaccinia virus. Nat Biotechnol 2006; 24(7): 817-9.
[14]
Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Informatics 2007; 8: 424.
[http://dx.doi.org/10.1186/1471-2105-8-424]
[15]
Jensen KK, Andreatta M, Buus S, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 154(3): 394-406.
[http://dx.doi.org/10.1111/imm.12889]
[16]
Doytchinova IA, Flower DR. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Informatics 2007; 8: 4.
[http://dx.doi.org/10.1186/1471-2105-8-4]
[17]
Bui HH, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics 2007; 8(1): 361.
[http://dx.doi.org/10.1186/1471-2105-8-361] [PMID: 17897458]
[18]
Bui HH, Sidney J, Dinh K, Southwood S, Newman MJ, Sette A. Predicting population coverage of T-cell epitope-based diagnostics and vaccines. BMC Bioinformatics 2006; 7(1): 153.
[http://dx.doi.org/10.1186/1471-2105-7-153] [PMID: 16545123]
[19]
Rahman N, Ali F, Basharat Z, et al. Vaccine design from the ensemble of surface glycoprotein epitopes of SARS-CoV-2: An immunoinformatics approach. Vaccines 2020; 8(3): 423.
[http://dx.doi.org/10.3390/vaccines8030423] [PMID: 32731461]
[20]
He J, Huang F, Zhang J, et al. Vaccine design based on 16 epitopes of SARS‐CoV‐2 spike protein. J Med Virol 2021; 93(4): 2115-31.
[http://dx.doi.org/10.1002/jmv.26596] [PMID: 33091154]
[21]
Chen X, Zaro JL, Shen WC. Fusion protein linkers: Property, design and functionality. Adv Drug Deliv Rev 2013; 65(10): 1357-69.
[http://dx.doi.org/10.1016/j.addr.2012.09.039] [PMID: 23026637]
[22]
Gasteiger E, Hoogland C, Gattiker A, et al. The Proteomics Protocols Handbook. Proteomics Protoc Handb 2005; pp. 571-608.
[http://dx.doi.org/10.1385/1-59259-890-0:571]
[23]
Källberg M, Margaryan G, Wang S, Ma J, Xu J. RaptorX server: A resource for template-based protein structure modeling. Methods Mol Biol 2014; 1137: 17-27.
[24]
Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods Enzymol 1997; 277: 396-404.
[25]
Colovos C, Yeates T. Verification of protein structures: Patterns of nonbonded atomic interactions. Protein Sci 1993; 2(9): 1511-9.
[26]
Wiederstein M, Sippl MJ. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 2007; 35 (Suppl. 2): W407-10.
[http://dx.doi.org/10.1093/nar/gkm290] [PMID: 17517781]
[27]
Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: A program to check the stereochemical quality of protein structures. J Appl Cryst 1993; 26(2): 283-91.
[http://dx.doi.org/10.1107/S0021889892009944]
[28]
Calis JJA, Maybeno M, Greenbaum JA, Weiskopf D, Silva AD De. Properties of MHC class i presented peptides that enhance immunogenicity. PLoS Comput Biol 2013; 9(10): e1003266.
[http://dx.doi.org/10.1371/journal.pcbi.1003266]
[29]
Dimitrov I, Bangov I, Flower DR, Doytchinova I. AllerTOP v.2-a server for in silico prediction of allergens. J Mol Model 2014; 20(6): 2278.
[http://dx.doi.org/10.1007/s00894-014-2278-5] [PMID: 24878803]
[30]
Dimitrov I, Naneva L, Doytchinova I, Bangov I. Systems biology AllergenFP : Allergenicity prediction by descriptor fingerprints 2013; 2005: 1-6.
[31]
Potocnakova L, Bhide M, Pulzova LB. An introduction to B-cell epitope mapping and in silico epitope prediction. J Immunol Res 2016 2016.
[32]
Ponomarenko J, Bui HH, Li W, et al. ElliPro : A new structurebased tool for the prediction of antibody epitopes. 2008; 8: 1-8.
[33]
Magnan CN, Randall A, Baldi P. SOLpro: Accurate sequence-based prediction of protein solubility. Bioinformatics 2009; 25(17): 2200-7.
[http://dx.doi.org/10.1093/bioinformatics/btp386] [PMID: 19549632]
[34]
Akira S, Uematsu S, Takeuchi O. Pathogen recognition and innate immunity. Cell 2006; 124(4): 783-801.
[http://dx.doi.org/10.1016/j.cell.2006.02.015] [PMID: 16497588]
[35]
Pyne N, Paul S. Screening of medicinal plants unraveled the leishmanicidal credibility of Garcinia cowa; highlighting Norcowanin, a novel anti-leishmanial phytochemical through in-silico study. J Parasit Dis 2021; 46(1): 202-14.
[http://dx.doi.org/10.1007/s12639-021-01441-7] [PMID: 35299910]
[36]
Patel MC, Shirey KA, Pletneva LM, et al. Novel drugs targeting Toll-like receptors for antiviral therapy. Future Virol 2014; 9(9): 811-29.
[http://dx.doi.org/10.2217/fvl.14.70] [PMID: 25620999]
[37]
Vries SJ. De, Bonvin AMJJ. CPORT: A consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS One 2011; 6(3): e17695.
[http://dx.doi.org/10.1371/journal.pone.0017695]
[38]
van Zundert GCP, Rodrigues JPGLM, Trellet M, et al. The HADDOCK2.2 web server: User-friendly integrative modeling of biomolecular complexes. J Mol Biol 2016; 428(4): 720-5.
[http://dx.doi.org/10.1016/j.jmb.2015.09.014] [PMID: 26410586]
[39]
Xue LC, Rodrigues JP, Kastritis PL, Mjj A. Structural bioinformatics PRODIGY: A web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016; 32(23): 3676-8.
[40]
Grote A, Hiller K, Scheer M, et al. JCat: A novel tool to adapt codon usage of a target gene to its potential expression host. Nucleic Acids Res 2005; 33: W526-31.
[http://dx.doi.org/10.1093/nar/gki376]
[41]
Stratmann T. Cholera toxin subunit b as adjuvant-an accelerator in protective immunity and a break in autoimmunity. Vaccines 2015; 3(3): 579-96.
[http://dx.doi.org/10.3390/vaccines3030579] [PMID: 26350596]
[42]
Read RJ, Adams PD, Arendall WB, et al. Ways & Means A new generation of crystallographic validation tools for theprotein data bank 2011.
[43]
Khanmohammadi S, Rezaei N. Role of Toll‐like receptors in the pathogenesis of COVID‐19. J Med Virol 2021; 93(5): 2735-9.
[http://dx.doi.org/10.1002/jmv.26826] [PMID: 33506952]
[44]
Vangone A, Schaarschmidt J, Koukos P, et al. Large-scale prediction of binding affinity in protein-small ligand complexes: The PRODIGY-LIG web server. Bioinformatics 2019; 35(9): 1585-7.
[http://dx.doi.org/10.1093/bioinformatics/bty816] [PMID: 31051038]
[45]
Zhao L, Zhang M, Cong H. Advances in the study of HLA-restricted epitope vaccines. Hum Vaccin Immunother 2013; 9(12): 2566-77.
[http://dx.doi.org/10.4161/hv.26088] [PMID: 23955319]
[46]
Skwarczynski M, Toth I. Peptide-based synthetic vaccines. Chem Sci (Camb) 2016; 7(2): 842-54.
[http://dx.doi.org/10.1039/C5SC03892H] [PMID: 28791117]
[47]
Salvatori G, Luberto L, Maffei M, et al. SARS-CoV-2 Spike protein: An optimal immunological target for vaccines. J Transl Med 2020; 18(1): 222.
[http://dx.doi.org/10.1186/s12967-020-02392-y] [PMID: 32493510]
[48]
Zhang GL, Bozic I, Kwoh CK, August JT, Brusic V. Prediction of supertype-specific HLA class I binding peptides using support vector machines. J Immunol Methods 2007; 320: 143-54.
[http://dx.doi.org/10.1016/j.jim.2006.12.011]
[49]
Taylor P, Street M, Wt L, et al. Cholera toxin B subunit acts as a potent systemic adjuvant for HIV-1 DNA vaccination intramuscularly in mice 2015; 37-41.
[50]
Phongsisay V, Iizasa E, Hara H, Yoshida H. Evidence for TLR4 and FcRγ–CARD9 activation by cholera toxin B subunit and its direct bindings to TREM2 and LMIR5 receptors. Mol Immunol 2015; 66(2): 463-71.
[http://dx.doi.org/10.1016/j.molimm.2015.05.008] [PMID: 26021803]
[51]
Onofrio L, Caraglia M, Facchini G, Margherita V, De S. Toll-like receptors and COVID-19: A two-faced story with an exciting ending. Future Sci OA 2020; 6(8): 10-3.
[http://dx.doi.org/10.2144/fsoa-2020-0091]
[52]
Aboudounya MM, Heads RJ. Review article COVID-19 and Toll-like receptor 4 (TLR4): SARS-CoV-2 may bind and activate TLR4 to increase ACE2 expression, facilitating entry and causing hyperinflammation. Mediators Inflamm 2021; 8874339.
[53]
Kar T, Narsaria U, Basak S, et al. A candidate multi-epitope vaccine against SARS-CoV-2. Sci Rep 2020; 10(1): 10895.
[http://dx.doi.org/10.1038/s41598-020-67749-1] [PMID: 32616763]
[54]
Purcell AW, McCluskey J, Rossjohn J. More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 2007; 6(5): 404-14.
[http://dx.doi.org/10.1038/nrd2224] [PMID: 17473845]
[55]
Oscherwitz J. The promise and challenge of epitope-focused vaccines. Hum Vaccin Immunother 2016; 12(8): 2113-6.
[http://dx.doi.org/10.1080/21645515.2016.1160977] [PMID: 27058686]
[56]
Heitmann JS, Bilich T, Tandler C, et al. A COVID-19 peptide vaccine for the induction of SARS-CoV-2 T cell immunity. Nature 2022; 601(7894): 617-22.
[http://dx.doi.org/10.1038/s41586-021-04232-5] [PMID: 34814158]
[57]
Ryzhikov AB. ,Ryzhikov ЕА, Bogryantseva MP, et al. A single blind, placebo-controlled randomized study of the safety, reactogenicity and immunogenicity of the “EpiVacCorona” Vaccine for the prevention of COVID-19, in volunteers aged 18–60 years (phase I–II). Infektsiia Immun 2021; 11(2): 283-96.
[http://dx.doi.org/10.15789/2220-7619-ASB-1699]
[58]
Ryzhikov AB, Ryzhikov EA, Bogryantseva MP, et al. Original study immunogenicity and protectivity of the peptide vaccine against SARS-CoV-2 2021; 76(1)
[59]
Solanki V, Tiwari M, Tiwari V. Prioritization of potential vaccine targets using comparative proteomics and designing of the chimeric multi-epitope vaccine against Pseudomonas aeruginosa. Sci Rep 2019; 9(1): 5240.
[http://dx.doi.org/10.1038/s41598-019-41496-4] [PMID: 30918289]
[60]
Dar HA, Zaheer T, Shehroz M, et al. Immunoinformatics-aided design and evaluation of a potential multi-epitope vaccine against Klebsiella Pneumoniae. Vaccines 2019; 7(3): 88.
[http://dx.doi.org/10.3390/vaccines7030088] [PMID: 31409021]
[61]
Ali M, Pandey RK, Khatoon N, Narula A, Mishra A, Prajapati VK. Exploring dengue genome to construct a multi-epitope based subunit vaccine by utilizing immunoinformatics approach to battle against dengue infection. Sci Rep 2017; 7(1): 9232.
[http://dx.doi.org/10.1038/s41598-017-09199-w] [PMID: 28835708]
[62]
Ojha R, Pareek A, Pandey RK, Prusty D, Prajapati VK. Strategic development of a next-generation multi-epitope vaccine to prevent nipah virus zoonotic infection. ACS Omega 2019; 4(8): 13069-79.
[http://dx.doi.org/10.1021/acsomega.9b00944]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy