Open Science Discovery of Potent Non-Covalent SARS-CoV-2 Main Protease Inhibitors

  1. Melissa L. Boby
  2. Daren Fearon
  3. Matteo Ferla
  4. Mihajlo Filep
  5. Lizbé Koekemoer
  6. Matthew C. Robinson
  7. The COVID Moonshot Consortium
  8. John D. Chodera
  9. Alpha A Lee
  10. Nir London
  11. Annette von Delft
  12. Frank von Delft

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Abstract

We report the results of the COVID Moonshot , a fully open-science, crowd sourced, structure-enabled drug discovery campaign targeting the SARS-CoV-2 main protease. We discovered a non-covalent, non-peptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>840 ligand-bound X-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2,400 compounds) for this campaign were shared rapidly and openly, creating a rich open and IP-free knowledgebase for future anti-coronavirus drug discovery.

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  1. Version published to 10.1101/2020.10.29.339317v5 on bioRxiv
  2. Version published to 10.1101/2020.10.29.339317v4 on bioRxiv
  3. Version published to 10.1101/2020.10.29.339317v3 on bioRxiv
  4. Version published to 10.1101/2020.10.29.339317v2 on bioRxiv
  5. ScreenIT

    SciScore for 10.1101/2020.10.29.339317: (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
    Percentage conversion (product peak integral / (product peak integral + substrate peak integral))*100) and percentage inhibitions were calculated and normalised against DMSO control with deduction of any background signal in Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    IC50s were calculated using Levenberg-Marquardt algorithm used to fit a restrained Hill equation to the dose-response data with both GraphPad PRISM and CDD. 1.4 Saturation Transfer Difference NMR (STD-NMR) assay: Method described in Kantsadi and Vakonakis [5].
    GraphPad PRISM
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

  6. Version published to 10.1101/2020.10.29.339317v1 on bioRxiv