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Maali A, Teimouri H, Azad M, Amiri S, Adibzadeh S. In-silico Immunomodelling of SARS-CoV-2. JoMMID 2021; 9 (2) :88-96
URL: http://jommid.pasteur.ac.ir/article-1-326-en.html
In-silico Immunomodelling of SARS-CoV-2
Amirhosein Maali , Hossein Teimouri , Mehdi Azad , Shahin Amiri , Setare Adibzadeh
Student Research Committee, Pasteur Institute of Iran, Tehran, Iran; Department of Medical Biotechnology, Qazvin University of Medical Sciences, Qazvin, Iran
Abstract:   (1568 Views)
Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense single-strand RNA virus belonging to the Coronaviridae family, responsible for coronavirus infectious disease 2019 (COVID-19) with the rapid transmission. This study aimed to characterize and compare SARS-CoV-2 and SARS-CoV major viral proteins and predict antigen proteasomal cleavage patterns, MHC class I processing and presentation, and B T-cell and anti-inflammatory epitopes. Methods: The amino acid sequences of spike surface (S) glycoprotein, membrane (M) glycoprotein, envelop (E) protein, and nucleocapsid (N) phosphoprotein was obtained from NCBI. The sequences were aligned by MEGA 7.0 and modeled by SWISS-MODEL. The proteasomal cleavage pattern, MHC class I processing, and T-cell epitopes were predicted via IEDB analysis and EPISOFT. The B-cell epitopes were predicted by BepiPred 2.0. Also, the prediction of anti-inflammatory epitopes was performed by AntiInflam. Results: Two major antigen proteins, S glycoprotein and M glycoprotein of SARS-CoV-2, respectively, showed 26.57% and 20.59% less efficiency in proteasomal cleavage and presentation to MHC class I, comparing SARS-CoV. There were fewer B-cell predicted epitopes in SARS-CoV-2, comparing SARS-CoV. The anti-inflammatory properties of SARS-CoV-2 S glycoprotein and N protein were higher than SARS-CoV. Conclusion: It seems that the evolution of SARS-CoV-2 is on the way to reducing antigen-presenting to MHC class I and escaping cellular immunity. Moreover, the predicted hotspot epitopes potentially can be used to induce adaptive cellular immunity against SARS-CoV-2. Besides, SARS-CoV-2 appears to be less immunopathogenic than SARS-CoV due to its higher anti-inflammatory proteins.
Keywords: SARS-CoV-2, Bioinformatics, T cell epitopes, B cell epitopes
Full-Text [PDF 2285 kb]   (804 Downloads)    
Type of Study: Original article | Subject: Other
Received: 2020/12/4 | Accepted: 2021/06/20 | Published: 2021/08/29
References
1. World Health Organization. Novel Coronavirus (‎ SARS-CoV-2)‎: situation report 52. 2020. Avaiable from: https://www.who.int/docs/default-source/coronaviruse/20200312-sitrep-52-covid-19.pdf
2. Garg D, Srivastava AK, Dhamija RK. Beyond Fever, Cough and Dyspnea: The Neurology of COVID-19. J Assoc Physicians India. 2020; 68 (9): 62-6. [DOI:10.4103/0028-3886.289000]
3. Paces J, Strizova Z, Smrz D, Cerny J. COVID-19 and the immune system. Physiol Res. 2020;69 (3): 379-88. [DOI:10.33549/physiolres.934492]
4. Oxenius A, Bachmann MF, Zinkernagel RM, Hengartner HJEjoi. Virus‐specific major MHC class II‐restricted TCR‐transgenic mice: effects on humoral and cellular immune responses after viral infection. Eur Journal Immunol. 1998; 28 (1): 390-400. https://doi.org/10.1002/(SICI)1521-4141(199801)28:01<390::AID-IMMU390>3.0.CO;2-O [DOI:10.1002/(SICI)1521-4141(199801)28:013.0.CO;2-O]
5. Naqvi AAT, Fatima K, Mohammad T, Fatima U, Singh IK, Singh A, 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. [DOI:10.1016/j.bbadis.2020.165878]
6. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018; 46 (W1): W296-W303. [DOI:10.1093/nar/gky427]
7. Guex N, Peitsch M.C, Schwede T. Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis. 2009; 30; 162-73. [DOI:10.1002/elps.200900140]
8. vBenkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011; 27 (3): 343-50. [DOI:10.1093/bioinformatics/btq662]
9. Bertoni M, Kiefer F, Biasini M, Bordoli L, Schwede T. Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology. Sci Rep. 2017; 7 (1): :10480. [DOI:10.1038/s41598-017-09654-8]
10. Kim Y, Ponomarenko J, Zhu Z, Tamang D, Wang P, Greenbaum J, et al. Immune epitope database analysis resource. Nucleic Acids Res. 2012; 40: W525-30. [DOI:10.1093/nar/gks438]
11. Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003; 12 (5): 1007-17. [DOI:10.1110/ps.0239403]
12. Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC-3.0: Accurate web accessible predictions of Human, Mouse, and Monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res. 2008; 36: W509-512. [DOI:10.1093/nar/gkn202]
13. Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics. 2016; 32 (4): 511-7. [DOI:10.1093/bioinformatics/btv639]
14. Peters B, Sette A. Generating quantitative models describing the sequence specificity of biological processes with the stabilized matrix method. BMC Bioinformatics. 2005; 31; 6: 132. [DOI:10.1186/1471-2105-6-132]
15. Sidney J, Assarsson E, Moore C, Ngo S, Pinilla C, Sette A, et al. Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries. Immunome Res. 2008; 4: 2. [DOI:10.1186/1745-7580-4-2]
16. Molero-Abraham M, Lafuente EM1, Flower DR, Reche PA. Selection of conserved epitopes from hepatitis C virus for pan-populational stimulation of T-cell responses. Clin Dev Immunol. 2013; 2013: 601943. [DOI:10.1155/2013/601943]
17. Martin Closter Jespersen, Bjoern Peters, Morten Nielsen, Paolo Marcatili. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017; 45 (W1): W24-W29. [DOI:10.1093/nar/gkx346]
18. Woodland DL. A focus on humoral immunity to viral infections. Viral Immunol. 2012; 25 (6): 441. [DOI:10.1089/vim.2012.ed.25.6]
19. Ghafouri F, Cohan RA, Noorbakhsh F, Samimi H, Haghpanah V. An in-silico approach to develop of a multi-epitope vaccine candidate against SARS-CoV-2 envelope (E) protein. Res Sq. 2020. [DOI:10.21203/rs.3.rs-30374/v1]
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.