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. 2020 Sep;6(9):e05001.
doi: 10.1016/j.heliyon.2020.e05001. Epub 2020 Sep 21.

Selective pressure on SARS-CoV-2 protein coding genes and glycosylation site prediction

Alessandra Lo Presti  1 , Giovanni Rezza  1   2 , Paola Stefanelli  1
Affiliations

Selective pressure on SARS-CoV-2 protein coding genes and glycosylation site prediction

Alessandra Lo Presti et al. Heliyon. 2020 Sep.

Abstract

Background: An outbreak of a febrile respiratory illness due to the newly discovered Coronavirus, SARS-CoV-2, was initially detected in mid-December 2019 in the city of Wuhan, Hubei province (China). The virus then spread to most countries in the world. As an RNA virus, SARS-CoV-2 may acquire mutations that may be fixed. The aim of this study was to evaluate the selective pressure acting on SARS-CoV-2 protein coding genes.

Methods: Mutations and glycosylation site prediction were analyzed in SARS-CoV-2 genomes (from 464 to 477 sequences).

Results: Selective pressure on surface glycoprotein (S) revealed one positively selected site (AA 943), located outside the receptor binding domain (RBD). Mutation analysis identified five residues on the surface glycoprotein, with variations (AA positions 367, 458, 477, 483, 491) located inside the RDB. Positive selective pressure was identified in nsp2, nsp3, nsp4, nsp6, nsp12, helicase, ORF3a, ORF8, and N sub-sets. A total of 22 predicted N-glycosylation positions were found in the SARS-CoV-2 surface glycoprotein; one of them, 343N, was located within the RBD. One predicted N-glycosylation site was found in the M protein and 4 potential O-glycosylation sites in specific protein 3a sequences.

Conclusion: Overall, the data showed positive pressure and mutations acting on specific protein coding genes. These findings may provide useful information on: i) markers for vaccine design, ii) new therapeutic approach, iii) information to implement mutagenesis experiments to inhibit SARS-CoV-2 cell entry. The negative selection identified in SARS-CoV-2 protein coding genes may help the identification of highly conserved regions useful to implement new future diagnostic protocols.

Keywords: Bioinformatics; Evolutionary biology; Glycosylation; Infectious disease; Microbiology; Mutation; Public health; Receptor binding domain; SARS-CoV-2; Selective pressure; Virology.

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Figures

Figure 1
Figure 1
The representative alignment for the comparison of the surface glycoprotein between SARS-CoV-2, SARS-CoV and Bat SARS - like virus (including the first 20 SARS-CoV-2 sequences, in addition to SARS-CoV-2 references), focusing the attention on the relevant positions 472 (amino acid L or P in SARS COV), 479 (amino acid N in SARS CoV) and 487 (amino acid T or S) of SARS CoV.
Figure 2
Figure 2
A. The predicted N-glycosylation sites in SARS-CoV-2 surface glycoprotein sub-set, obtained by using N-GlycoSite tool. The positions, number and fraction of the predicted N-glycosylation sites were reported. B. The predicted N-glycosylation sites in SARS-CoV-2 M protein sub-set, obtained by using N-GlycoSite tool. The position, number and fraction of the predicted N-glycosylation sites were reported.
Figure 3
Figure 3
The predicted N-glycosylation sites in SARS-CoV-2 E protein sub-set obtained by using N-GlycoSite tool. The positions, number and fraction of the predicted N-glycosylation sites were reported.
See this image and copyright information in PMC

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