Identifying SARS-CoV-2 antiviral compounds by screening for small molecule inhibitors of nsp14/nsp10 exoribonuclease

  1. Berta Canal
  2. Allison W. McClure
  3. Joseph F. Curran
  4. Mary Wu
  5. Rachel Ulferts
  6. Florian Weissmann
  7. Jingkun Zeng
  8. Agustina P. Bertolin
  9. Jennifer C. Milligan
  10. Souradeep Basu
  11. Lucy S. Drury
  12. Tom D. Deegan
  13. Ryo Fujisawa
  14. Emma L. Roberts
  15. Clovis Basier
  16. Karim Labib
  17. Rupert Beale
  18. Michael Howell
  19. John F.X. Diffley

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Abstract

SARS-CoV-2 is a coronavirus that emerged in 2019 and rapidly spread across the world causing a deadly pandemic with tremendous social and economic costs. Healthcare systems worldwide are under great pressure, and there is an urgent need for effective antiviral treatments. The only currently approved antiviral treatment for COVID-19 is remdesivir, an inhibitor of viral genome replication. SARS-CoV-2 proliferation relies on the enzymatic activities of the non-structural proteins (nsp), which makes them interesting targets for the development of new antiviral treatments. With the aim to identify novel SARS-CoV-2 antivirals, we have purified the exoribonuclease/methyltransferase (nsp14) and its cofactor (nsp10) and developed biochemical assays compatible with high-throughput approaches to screen for exoribonuclease inhibitors. We have screened a library of over 5000 commercial compounds and identified patulin and aurintricarboxylic acid (ATA) as inhibitors of nsp14 exoribonuclease in vitro. We found that patulin and ATA inhibit replication of SARS-CoV-2 in a VERO E6 cell-culture model. These two new antiviral compounds will be valuable tools for further coronavirus research as well as potentially contributing to new therapeutic opportunities for COVID-19.

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  1. Version published to 10.1042/bcj20210198
  2. ScreenIT

    SciScore for 10.1101/2021.04.07.438812: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    SARS-CoV-2 production: Batches of the BetaCoV/England/02/2020 (Public Health England) strain of the SARS-CoV-2 virus were produced as in our accompanying manuscripts using VERO E6 cells.
    VERO E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    Expression constructs and protein expression in E.coli: The sequences of SARS-CoV-2 nsp14 and nsp10 (NCBI reference sequence NC_045512.2) were codon optimised for expression in E. coli using the GeneArt Gene Synthesis software (ThermoFisher scientific) and genes were ordered from GeneWiz (codon optimised DNA sequences in supplementary information).
    GeneWiz
    suggested: (GENEWIZ, RRID:SCR_003177)
    Screen data analysis: Screen data were analysed with custom MATLAB and R scripts.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    Slopes were then used to calculate KM and VMAX by non-linear fitting to the Michaelis-Menten equation using GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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.

  3. Version published to 10.1101/2021.04.07.438812v1 on bioRxiv