SARS-CoV2 spike protein displays biologically significant similarities with paramyxovirus surface proteins; a bioinformatics study

  1. Ehsan Ahmadi
  2. Mohammad Reza Zabihi
  3. Ramin Hosseinzadeh
  4. Farshid Noorbakhsh

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Abstract

Recent emergence of SARS-CoV2 and associated COVID-19 pandemic has posed a great challenge for the scientific community. Understanding various aspects of SARS-CoV2 biology, virulence and pathogenesis as well as determinants of immune response have become a global research priority. In this study, we performed bioinformatic analyses on SAR-CoV2 protein sequences, trying to unravel biologically important similarities between this newly emerged virus with other RNA viruses. Comparing the proteome of SARS-CoV2 with major positive and negative strand ssRNA viruses showed significant homologies between SARS-CoV2 spike protein with pathogenic paramyxovirus fusion proteins. This ‘spike-fusion’ homology was not limited to SARS-CoV2 and it existed for some other pathogenic coronaviruses; nonetheless, SARS-CoV2 spike-fusion homology was orders of magnitude stronger than homologies observed for other known coronaviruses. Moreover, this homology did not seem to be a consequence of general ssRNA virus phylogenetic relations. We also explored potential immunological significance of this spike-fusion homology. Spike protein epitope analysis using experimentally verified data deposited in Immune Epitope Database (IEDB) revealed that the majority of spike’s T cell epitopes as well as many B cell and MHC binding epitopes map within the spike-fusion homology region. Overall, our data indicate that there might be a relation between SARS-CoV2 and paramyxoviruses at the level of their surface proteins and this relation could be of crucial immunological importance.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.20.210534: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Extracting SARS-CoV2 and other coronavirus protein sequences: SARS-CoV2 Reference Protein sequences were obtained from NCBI Protein database (
    NCBI Protein
    suggested: (NCBI Protein, RRID:SCR_003257)
    https://www.ncbi.nlm.nih.gov/protein).
    https://www.ncbi.nlm.nih.gov/protein
    suggested: (Protein Database, RRID:SCR_017486)
    RefSeq accession numbers and their associated official gene games are shown in Supplemental Table 1.
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Pairwise BLASTP search was used to find the distribution of these epitopes on spike protein.
    BLASTP
    suggested: (BLASTP, RRID:SCR_001010)
    Multiple sequence alignment (MSA) and phylogenetic analysis were performed using MEGA X and PhyML(10).
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    MUSCLE algorithm was used to perform MSA between sequences.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    Phylogenetic tree construction was performed in PhyML and MEGAX.
    PhyML
    suggested: (PhyML, RRID:SCR_014629)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.07.20.210534v1 on bioRxiv