SARS-CoV-2 structural coverage map reveals state changes that disrupt host immunity

  1. Seán I. O’Donoghue
  2. Andrea Schafferhans
  3. Neblina Sikta
  4. Christian Stolte
  5. Sandeep Kaur
  6. Bosco K. Ho
  7. Stuart Anderson
  8. James Procter
  9. Christian Dallago
  10. Nicola Bordin
  11. Matt Adcock
  12. Burkhard Rost

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Abstract

In response to the COVID-19 pandemic, many life scientists are focused on SARS-CoV-2. To help them use available structural data, we systematically modeled all viral proteins using all related 3D structures, generating 872 models that provide detail not available elsewhere. To organise these models, we created a structural coverage map: a novel, one-stop visualization summarizing what is — and is not — known about the 3D structure of the viral proteome. The map highlights structural evidence for viral protein interactions, mimicry, and hijacking; it also helps researchers find 3D models of interest, which can then be mapped with UniProt, PredictProtein, or CATH features. The resulting Aquaria-COVID resource ( https://aquaria.ws/covid ) helps scientists understand molecular mechanisms underlying coronavirus infection. Based on insights gained using our resource, we propose mechanisms by which the virus may enter immune cells, sense the cell type, then switch focus from viral reproduction to disrupting host immune responses.

Significance

Currently, much of the COVID-19 viral proteome has unknown molecular structure. To improve this, we generated ∼1,000 structural models, designed to capture multiple states for each viral protein. To organise these models, we created a structure coverage map: a novel, one-stop visualization summarizing what is — and is not — known about viral protein structure. We used these data to create an online resource, designed to help COVID-19 researchers gain insight into the key molecular processes that drive infection. Based on insights gained using our resource, we speculate that the virus may sense the type of cells it infects and, within certain cells, it may switch from reproduction to disruption of the immune system.

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    SciScore for 10.1101/2020.07.16.207308: (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
    SARS-CoV-2 Sequences: This study was based on the 14 protein sequences provided in UniProtKB/Swiss-Prot version 2020_03 (released April 22, 2020; https://www.uniprot.org/statistics/) as comprising the SARS-CoV-2 proteome.
    UniProtKB/Swiss-Prot
    suggested: None
    The recovery rate, i.e. the ratio of proteins from the CATH nr40 data (with less than 40% sequence identity) found by our method that have the same CATH code, was slightly higher with HH-suite3 (8) (20.8% vs. 19.4%).
    CATH
    suggested: None
    PredictProtein Features: To facilitate analysis of SARS-CoV-2 sequences, we enhanced the Aquaria resource to include PredictProtein features (18), thus providing a very rich set of predicted features for all Swiss-Protein sequences.
    PredictProtein
    suggested: None

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Thus, the small number of interactions found in this work likely indicates limitations in current structural data. Using the current structural data (Fig. 2), we can divide the 27 SARS-CoV-2 proteins into four categories: mimics, hijackers, teams, and suspects — below, we highlight insights derived in this work for each of these categories. Mimics: We found structural evidence for mimicry of human proteins for only two SARS-CoV-2 proteins: NSP3 and NSP13 (Figs. 2 & 3; Table S6). NSP3 may mimic host proteins containing macro domains, interfering with ADP-ribose (ADPr) modification and thereby suppressing host innate immunity (22). We found nine potentially mimicked proteins (Fig. 3A); the top ranked matches (MACROD2 and MACROD1) remove ADPr from proteins (47), reversing the effect of ADPr writers (e.g., PARP9, PARP14, and PARP15 in lymphoid tissues), and affecting ADPr readers (e.g., the core histone proteins MACROH2A1, and MACROH2A2, found in most cells). Thus we speculate that, in infected cells, ADPr erasure by NSP3 may influence epigenetic regulation of chromatin state (48), potentially contributing to variation in COVID-19 patient outcomes. Furthermore, in infected macrophages, activation by PARP9 and PARP14 may be undermined by NSP3’s erasure of ADPr, resulting in vascular disorders (49), as seen in COVID-19 (50). NSP13 may mimic four human helicases, based on stronger alignment evidence than for mimicry by NSP3 (Fig. 3). Three of these helicases are associated with DNA ...

    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.

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  2. Version published to 10.1101/2020.07.16.207308 on bioRxiv