A multi-epitope self-amplifying mRNA SARS-CoV-2 vaccine design using a reverse vaccinology approach

Brigitta Claudia, Husna Nugrahapraja, Ernawati Arifin Giri-Rachman

Abstract


Background and purpose: Massive vaccine distribution is a crucial step to prevent the spread of SARS-CoV2 as the causative agent of COVID-19. This research aimed to design the multi-epitope self-amplifying mRNA (saRNA) vaccine from the spike and nucleocapsid proteins of SARS-CoV2.

Experimental approach: Commonly distributed constructions class I and II alleles of the Indonesian population were used to determine peptide sequences that trigger this population’s high specificity T-cell response. The best vaccine candidate was selected through the analysis of tertiary structure validation and molecular docking of each candidate with TLR-4, TLR-8, HLA-A*24:02, and HLA-DRB1*04:05. The selected multi-epitope vaccine combined with the gene encoding the replication machinery that allows the RNA amplification in the host cell.

Findings/Results: Seven B-cell and four T-cell epitopes from the protein target were highly antigenic and conserved, non-allergen, non-toxic, and hydrophilic. Tertiary structure validation then determined the best multi-epitope construction with 269 AA in length containing hBD-2 adjuvant and PADRE. Most residues are predicted to be accessible by solvent and show high population coverage (99,26%). Molecular docking analysis demonstrated a stable and strong binding affinity with immune receptors. A recombinant plasmid as the template for mRNA production was constructed by inserting the multi-epitope DNA and non-structural polyprotein 1-4 gene of VEEV, which encodes the RNA replication complex to the cloning site of pcDNA3.1(+).

Conclusion and implication: In silico, design of self-amplifying mRNA could be a potential COVID-19 vaccine candidate since its ability to be amplified in the host cell can efficiently reduce the intake doses.

 

 


Keywords


Antigenic; COVID-19; Immunogenic; mRNA Vaccine; Sequences.

Full Text:

PDF

References


Jackson NA, Kester KE, Casimiro D, Gurunathan S, DeRosa F. The promise of mRNA vaccines: a biotech and industrial perspective. NPJ Vaccines. 2020;5(1):1-6.DOI: 10.1038/s41541-020-0159-8.

Gergen J, Petsch B. mRNA-based vaccines and mode of action. Curr Top Microbiol Immunol. 2022;440: 1-30.DOI: 10.1007/82_2020_230.

Bloom K, van den Berg F, Arbuthnot P. Self-amplifying RNA vaccines for infectious diseases. Gene Ther. 2021;28(3-4):117-129.DOI: 10.1038/s41434-020-00204-y.

Pardi N, Hogan MJ, Porter FW, Weissman D. mRNA vaccines-a new era in vaccinology. Nat Rev Drug Discov. 2018;17(4):261-279. DOI: 10.1038/nrd.2017.243.

Elbe S, Buckland‐Merrett G. Data, disease and diplomacy:GISAID’s innovative contribution to global health. Glob Chall. 2017;1(1):33-46. DOI: 10.1002/gch2.1018.

Katoh K, Rozewicki J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 2019;20(4):1160-1166.DOI: 10.1093/bib/bbx108.

Tamura K, Dudley J, Nei M, Kumar S. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24(8):1596-1599.DOI: 10.1093/molbev/msm092.

Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47(W1):W636-W641.DOI: 10.1093/nar/gkz268.

Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006;65(1):40-48.DOI: 10.1002/prot.21078.

Stranzl T, Larsen MV, Lundegaard C, Nielsen M. NetCTLpan:pan-specific MHC class I pathway epitope predictions. Immunogenetics. 2010;62(6):357-368.DOI: 10.1007/s00251-010-0441-4.

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(3):394-406.DOI: 10.1111/imm.12889.

Doytchinova IA, Flower DR. VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics. 2007;8:4:1-7.DOI: 10.1186/1471-2105-8-4.

Dimitrov I, Naneva L, Doytchinova I, Bangov I. AllergenFP:allergenicity prediction by descriptor fingerprints. Bioinformatics. 2014;30(6):846-851.DOI: 10.1093/bioinformatics/btt619.

Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GPS, et al. In silico approach for predicting toxicity of peptides and proteins. PLoS One. 2013;8(9):e73957,1-10. DOI: 10.1371/journal.pone.0073957.

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;8:361:1-6.DOI: 10.1186/1471-2105-8-361.

Hasan MA, Khan MA, Datta A, Mazumder MHH, Hossain MU. A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment. Mol Immunol. 2015;65(1):189-204.DOI: 10.1016/j.molimm.2014.12.013.

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;7:153,1-5.DOI: 10.1186/1471-2105-7-153.

Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res. 2016;44(W1):W449-W454.DOI: 10.1093/nar/gkw329.

de Vries SJ, JBonvin AMJ. CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS One. 2011;6(3):e17695,1-12.DOI: 10.1371/journal.pone.0017695.

Van Zundert GCP, Rodrigues JPGLM, Trellet M, Schmitz C, Kastritis PL, Karaca, E, et al. The HADDOCK2. 2 web server: user-friendly integrative modeling of biomolecular complexes. J Mol Biol. 2016;428(4):720-725.DOI: 10.1016/j.jmb.2015.09.014.

Saadi M, Karkhah A, Nouri HR. Development of a multi-epitope peptide vaccine inducing robust T cell responses against brucellosis using immunoinformatics based approaches. Infect Genet Evol. 2017;51:227-234.DOI: 10.1016/j.meegid.2017.04.009.

Yang Y, Sun W, Guo J, Zhao G, Sun S, Yu H, et al. In silico design of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations. Hum Vaccin Immunother. 2015;11(3):795-805.DOI: 10.1080/21645515.2015.1012017.

Buchan DWA, Jones DT. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res. 2019;47(W1):W402-W407. DOI: 10.1093/nar/gkz297.

Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D. Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci USA. 2020;117(3):1496-1503. DOI: 10.1073/pnas.1914677117.

Bhattacharya D, Nowotny J, Cao R, Chen J. 3Drefine: an interactive web server for efficient protein structure refinement. Nucleic Acids Res. 2016;44(W1):W406-W409.DOI: 10.1093/nar/gkw336.

DeLano WL. PyMOL: an open-source molecular graphics tool. CCP4 Newsletter on protein crystallography. 2002;40(1):82-92. Available at: https://www.pymol.org/

Williams CJ, Headd JJ, Moriarty NW, Prisant MG, Videau LL, Deis LN et al. MolProbity: more and better reference data for improved all‐atom structure validation. Protein Sci. 2018;27(1):293-315.DOI: 10.1002/pro.3330.

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(Web Server issue):W407-W410. DOI: 10.1093/nar/gkm290.

Mashiach E, Schneidman-Duhovny D, Andrusier N, Nussinov R, Wolfson HJ. FireDock: a web server for fast interaction refinement in molecular docking. Nucleic Acids Res. 2008;36(Web Server issue):W229-W232. DOI: 10.1093/nar/gkn186.

Weng G, Wang E, Wang Z, Liu H, Zhu F, Li D, Hou T. HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 2019;47(W1):W322-W330. DOI: 10.1093/nar/gkz397.

Laskowski RA, Jabłońska, J, Pravda L, Vařeková RS, Thornton JM. PDBsum: structural summaries of PDB entries. Protein Sci. 2018;27(1):129-134.DOI: 10.1002/pro.3289.

López-Blanco JR, Aliaga, JI, Quintana-Ortí, ES, Chacón, P. iMODS: internal coordinates normal mode analysis server. Nucleic Acids Res. 2014;42(Web Server issue):W271-W276.DOI: 10.1093/nar/gku339.

Sharma A, Knollmann-Ritschel, B. Current understanding of the molecular basis of venezuelan equine encephalitis virus pathogenesis and vaccine development. Viruses. 2019;11(2):164,1-32.DOI: 10.3390/v11020164.

Kurkcuoglu Z, Koukos PI, Citro N, Trellet ME, Rodrigues JPGLM, Moreira IS, et al. Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2. J Comput Aided Mol Des. 2018;32(1):175-185. DOI: 10.1007/s10822-017-0049-y.

Du X, Li Y, Xia YL, Ai SM, Liang J, Sang P, et al. Insights into protein-ligand interactions: mechanisms, models, and methods. Int J Mol Sci. 2016;17(2):144,1-34.DOI: 10.3390/ijms17020144.

Han C, Kawana-Tachikawa A, Shimizu A, Zhu D, Nakamura H, Adachi E, et al. Switching and emergence of CTL epitopes in HIV-1 infection. Retrovirology. 2014;11:38,1-15.DOI: 10.1186/1742-4690-11-38.

Ting YT, Petersen J, Ramarathinam SH, Scally SW, Loh KL, Thomas R, et al. The interplay between citrullination and HLA-DRB1 polymorphism in shaping peptide binding hierarchies in rheumatoid arthritis. J Biol Chem. 2018;293(9):3236-3251.DOI: 10.1074/jbc.RA117.001013.

Grosdidier S, Fernández-Recio J. Identification of hotspot residues in protein-protein interactions by computational docking. BMC Bioinformatics. 2008;9:447,1-13. DOI: 10.1186/1471-2105-9-447.

Panteri R, Paiardini A, Keller F. A 3D model of Reelin subrepeat regions predicts Reelin binding to carbohydrates. Brain Res. 2006;1116(1):222-230. DOI: 10.1016/j.brainres.2006.07.128.

Vangone A, Bonvin AMJJ. Contacts-based prediction of binding affinity in protein-protein complexes. Elife. 2015;4:e07454,1-15.

DOI: 10.7554/eLife.07454.

Vreven T, Hwang H, Pierce BG, Weng Z. Evaluating template-based and template-free protein-protein complex structure prediction. Brief Bioinform. 2014;15(2):169-176. PMID: 23818491.

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(3):151955,1-18.DOI: 10.1016/j.imbio.2020.151955.

Kumar J, Qureshi R, Sagurthi SR, Qureshi IA. Designing of nucleocapsid protein based novel multi-epitope vaccine against SARS-COV-2 using immunoinformatics approach. Int J Pept Res Ther. 2021;27(2):941-956.

DOI: 10.1007/s10989-020-10140-5.

Kulasegaran-Shylini R, Atasheva S, Gorenstein DG, Frolov I. Structural and functional elements of the promoter encoded by the 5′ untranslated region of the venezuelan equine encephalitis virus genome. J Virol. 2009;83(17):8327-8339.DOI: 10.1128/JVI.00586-09.

Puigbò P, Bravo IG, Garcia-Vallve S. CAIcal: a combined set of tools to assess codon usage adaptation. Biol Direct. 2008;3:38,1-8.DOI: 10.1186/1745-6150-3-38.

Agnihothram S, Menachery VD, Yount Jr BL, Lindesmith LC, Scobey T, Whitmore A, et al. Development of a broadly accessible Venezuelan equine encephalitis virus replicon particle vaccine platform. J Virol. 2018;92(11):e00027-18:1-38.DOI: 10.1128/JVI.00027-18.

Araujo MB, Naimi B. Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate. MedRxiv. 2020;1-15.DOI: 10.1101/2020.03.12.20034728.

Xiaojie S, Yu L, Guang Y, Min Q. Neutralizing antibodies targeting SARS-CoV-2 spike protein. Stem Cell Res. 2021;50:102125,1-11.DOI: 10.1016/j.scr.2020.102125.

Martinez IL, Llinás DT, Romero MPB, Salazar LM. High mutation rate in SARS-CoV-2:will it hit us the same way forever. J Infect Dis Epidemiol. 2020;6(6):1-2.DOI: 10.23937/2474-3658/1510176.

Dutta NK, Mazumdar K, Gordy JT. The nucleocapsid protein of SARS-CoV-2: a target for vaccine development. J Virol. 2020;94(13):1-2.DOI: 10.1128/JVI.00647-20.

Skwarczynski M, Tot I. Peptide-based synthetic vaccines. Chem Sci. 2016;7(2): 842-854. DOI: 10.1039/c5sc03892h.

Ilinskaya AN, Dobrovolskaia MA. Understanding the immunogenicity and antigenicity of nanomaterials: past, present and future. Toxicol Appl Pharmacol. 2016;299:70-77.DOI: 10.1016/j.taap.2016.01.005.

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;10(1):10895, 1-130.DOI: 10.1038/s41598-020-67749-1.

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):1-19.DOI: 10.1038/s41598-019-41496-4.

Yasmin T, Akter S, Debnath M, Ebihara A, Nakagawa T, Nabi AHMN. In silico proposition to predict cluster of B-and T-cell epitopes for the usefulness of vaccine design from invasive, virulent and membrane-associated proteins of C. jejuni. In Silico Pharmacol. 2016;4(1):5,1-10.DOI: 10.1186/s40203-016-0020-y.

Wu W, Wang Z, Cong P, Li T. Accurate prediction of protein relative solvent accessibility using a balanced model. BioData Min. 2017; 10:1,1-14.DOI: 10.1186/s13040-016-0121-5.

Aboudounya MM, Heads RJ. 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;2021:1-18.DOI: 10.1155/2021/8874339.

Ohto U, Tanji H, Shimizu T. Structure and function of toll-like receptor 8. Microbes Infect. 2014;16(4):273-282.DOI: 10.1016/j.micinf.2014.01.007.

Yuliwulandari R, Kashiwase K, Nakajima H, Uddin J, Susmiarsih TP, Sofro ASM, et al. Polymorphisms of HLA genes in western Javanese (indonesia): close affinities to southeast Asian populations. Tissue Antigens. 2009;73(1):46-53.DOI: 10.1111/j.1399-0039.2008.01178.x.


Refbacks

  • There are currently no refbacks.


Creative Commons LicenseThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License which allows users to read, copy, distribute and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited properly.