Abstract

  Increasing transmissibility and pathogenicity of SARS-CoV-2 has often been attributed to mutations accumulating in RNA genome of SARS-CoV-2 ancestral type originated in China. In this study, using in silico structural modeling and molecular docking, we demonstrate that observed low frequency non-synonymous amino acid alterations do not improve the efficacy of interaction between SARS-CoV-2 spike glycoprotein and host’s ACE2 receptor. The major spike haplotype detected corresponds to the ancestral Wuhan isolate which displays the most favorable interaction with ACE2. Although the sequences are different, length and number of predicted B-cell epitopes of SARS-CoV-2 spike protein, identified using BepiPred 2.0, are comparable to that of SARS-CoV. However, the above data differ from the ones from HCoV-NL63. Analysis of population genetic parameters implies relaxed purifying selection to be predominant force shaping the genetic landscape of SARS-CoV-2 spike protein, till date. These findings provide important clues in designing therapeutic strategies to contain the pandemic.

Keywords: SARS-CoV-2; Spike; ACE2; Epitopes; Purifying Selection

References

1. Coronavirus disease (COVID-19) pandemic.
2. Menachery VD., et al. “Jumping species-a mechanism for coronavirus persistence and survival”. Current Opinion on Virology 23 (2017): 1-7.
3. Weiss SR and Navas-martin S. “Coronavirus pathogenesis and the emerging pathogen severe acute respiratory syndrome coronavirus”. Microbiology and Molecular Biology Reviews 4 (2005): 635-664.
4. Gralinski LE and Menachery VD. “Return of the Coronavirus: 2019-nCoV”. Viruses 2 (2020).
5. Cui J., et al. “Origin and evolution of pathogenic coronaviruses”. Nature Review Microbiology3 (2019): 181-192.
6. Li Q., et al. “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia”. The New England Journal of Medicine13 (2020): 1199-1207.
7. Zhou P., et al. “A pneumonia outbreak associated with a new coronavirus of probable bat origin”. Nature7798 (2020): 270-273.
8. Belouzard S., et al. “Mechanisms of coronavirus cell entry mediated by the viral spike protein”. Viruses6 (2012): 1011-1033.
9. Hulswit RJ., et al. “Coronavirus Spike Protein and Tropism Changes”. Advances in Virus Research 96 (2016): 29-57.
10. Madu IG., et al. “Characterization of a highly conserved domain within the severe acute respiratory syndrome coronavirus spike protein S2 domain with characteristics of a viral fusion peptide”. Journal of Virology 15 (2009): 7411-7421.
11. Heald-sargent T and Gallagher T. “Ready, set, fuse! The coronavirus spike protein and acquisition of fusion competence”. Viruses4 (2012): 557-580.
12. Mossel EC., et al. “Exogenous ACE2 expression allows refractory cell lines to support severe acute respiratory syndrome coronavirus replication”. Journal of Virology6 (2005): 3846-3850.
13. Hofmann H., et al. “Human coronavirus NL63 employs the severe acute respiratory syndrome coronavirus receptor for cellular entry”. Proceedings of the National Academy of Sciences of the United States of America 22 (2005): 7988-7993.
14. Basic local alignment search tool.
15. Rozas J., et al. “DnaSP 6: DNA Sequence Polymorphism Analysis of Large Data Sets”. Molecular Biology and Evolution 12 (2017): 3299-3302.
16. Kumar S., et al. “MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms”. Molecular Biology and Evolution 6 (2018): 1547-1549.
17. Mcdonald JH and Kreitman M. “Adaptive protein evolution at the Adh locus in Drosophila”. Nature 6328 (1991): 652-654.
18. RCSB protein database.
19. SWISS-MODEL.
20. Duhovny D., et al. “Efficient Unbound Docking of Rigid Molecules”. In: Guigó R., Gusfield D. (eds) Algorithms in Bioinformatics. WABI 2002. Lecture Notes in Computer Science, vol 2452. Springer, Berlin, Heidelberg (2002).
21. Schneidman-duhovny D., et al. “PatchDock and SymmDock: servers for rigid and symmetric docking”. Nucleic Acids Research 33 (2015).
22. Andrusier N., et al. “FireDock: fast interaction refinement in molecular docking”. Proteins 1 (2007): 139-159.
23. Mashiach E., et al. “FireDock: a web server for fast interaction refinement in molecular docking”. Nucleic Acids Research 36 (2008): W229-232.
24. Xue LC., et al. “PRODIGY: a web server for predicting the binding affinity of protein-protein complexes”. Bioinformatics 23 (2016): 3676-3678.
25. Jespersen MC., et al. “BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes”. Nucleic Acids Research 45 (2017): W24-W29.
26. Principi N., et al. “Effects of coronavirus infections in children”. Emerging Infection Disease 16 (2010): 183-188.
27. Raoult D., et al. “Coronavirus infections: Epidemiological, clinical and immunological features and hypotheses”. Cell Stress4 (2020): 66-75.
28. Brielle ES., et al. “The SARS-CoV-2 Exerts a Distinctive Strategy for Interacting with the ACE2 Human Receptor”. Viruses5 (2010).
29. Ortega JT., et al. “Role of changes in SARS-CoV-2 spike protein in the interaction with the human ACE2 receptor: An analysis”. EXCLI Journal 19 (2020): 410-417.
30. Bai Y., et al. “Presumed Asymptomatic Carrier Transmission of COVID-19”. JAMA14 (2020): 1406-1407.
31. Zou L., et al. “SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients”. The New England Journal of Medicine 382 (2020): 1177-79.

Citation

Citation: Pramita Chowdhury., et al. “In-depth Sequence Analysis of SARS-CoV-2 Spike Protein Repudiates Mutation Mediated Adaptive Selection in the Virus". Acta Scientific Microbiology 3.12 (2020): 16-23.

Copyright

Copyright: © 2020 Pramita Chowdhury., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.