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. 2022 Jun 14:13:891418.
doi: 10.3389/fgene.2022.891418. eCollection 2022.

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses

Vittoria Cicaloni  1 , Filippo Costanti  2 , Arianna Pasqui  1 , Monica Bianchini  2 , Neri Niccolai  3 , Pietro Bongini  2   4
Affiliations

A Bioinformatics Approach to Investigate Structural and Non-Structural Proteins in Human Coronaviruses

Vittoria Cicaloni et al. Front Genet. .

Abstract

Recent studies confirmed that people unexposed to SARS-CoV-2 have preexisting reactivity, probably due to previous exposure to widely circulating common cold coronaviruses. Such preexistent reactivity against SARS-CoV-2 comes from memory T cells that can specifically recognize a SARS-CoV-2 epitope of structural and non-structural proteins and the homologous epitopes from common cold coronaviruses. Therefore, it is important to understand the SARS-CoV-2 cross-reactivity by investigating these protein sequence similarities with those of different circulating coronaviruses. In addition, the emerging SARS-CoV-2 variants lead to an intense interest in whether mutations in proteins (especially in the spike) could potentially compromise vaccine effectiveness. Since it is not clear that the differences in clinical outcomes are caused by common cold coronaviruses, a deeper investigation on cross-reactive T-cell immunity to SARS-CoV-2 is crucial to examine the differential COVID-19 symptoms and vaccine performance. Therefore, the present study can be a starting point for further research on cross-reactive T cell recognition between circulating common cold coronaviruses and SARS-CoV-2, including the most recent variants Delta and Omicron. In the end, a deep learning approach, based on Siamese networks, is proposed to accurately and efficiently calculate a BLAST-like similarity score between protein sequences.

Keywords: SARS-CoV-2; Siamese networks; cross-reactivity; long short-term memories; similarity score.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Basic features of seven HCoVs. The two blue panels list alpha-HCoVs and the five orange panels list beta-HCoVs. All the boxes include the year of identification, receptor, and major proteins. Two beta-HCoVs (HCoV-OC43 and HCoV-HKU1) originate in mice and contain hemagglutinin-esterase (HE) structural protein along with S, E, M, and N. Other five HCoVs originated in bats and have four major structural proteins S, E, M, and N.
FIGURE 2
FIGURE 2
Distribution of similarity between pairs of examples in the data set. The number of pairs is proportional to similarity.
FIGURE 3
FIGURE 3
Distribution of protein sequence length in the data set.
FIGURE 4
FIGURE 4
Percent identity matrix. In (A), the percent identity matrix is reported, which shows the % of identities among the HCoVs. In (B), the similarity phylogram tree is shown. The branch length is proportional to the number of nucleotide substitutions, that is, to the number of evolutionary events that took place after the branching point.
FIGURE 5
FIGURE 5
Percent identity matrix. In A, the percent identity matrix is reported, which shows the % of identities among the S proteins of HCoVs.
See this image and copyright information in PMC

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