Discovery of Drug-Like Ligands for the Mac1 Domain of SARS-CoV-2 Nsp3

  1. Rajdeep S. Virdi
  2. Robert V. Bavisotto
  3. Nicholas C. Hopper
  4. Nemanja Vuksanovic
  5. Trevor R. Melkonian
  6. Nicholas R. Silvaggi
  7. David N. Frick

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  1. Version published to 10.1177/2472555220960428
  2. Version published to 10.1101/2020.07.06.190413v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.07.06.190413: (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
    Tm’s were calculated by fitting the data to equation 1 using either GraphPad Prism or TSA-CRAFT (https://sourceforge.net/projects/tsa-craft/).8

    In Eq. 1, Fobs (T) is the observed fluorescence at each temperature (T), Fmin is the minimum observed fluorescence, Fmax is maximum observed fluorescence, and a is a Hill slope.

    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Docking box location was configured prior to using AutoDock tools.
    AutoDock
    suggested: (AutoDock, RRID:SCR_012746)

    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.

  4. Version published to 10.1101/2020.07.06.190413v1 on bioRxiv