SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies

  1. Christopher O. Barnes
  2. Claudia A. Jette
  3. Morgan E. Abernathy
  4. Kim-Marie A. Dam
  5. Shannon R. Esswein
  6. Harry B. Gristick
  7. Andrey G. Malyutin
  8. Naima G. Sharaf
  9. Kathryn E. Huey-Tubman
  10. Yu E. Lee
  11. Davide F. Robbiani
  12. Michel C. Nussenzweig
  13. Anthony P. West
  14. Pamela J. Bjorkman

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Abstract

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  1. Version published to 10.1038/s41586-020-2852-1
  2. ScreenIT

    SciScore for 10.1101/2020.08.30.273920: (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

    Antibodies
    SentencesResources
    Polyreactivity was quantified by detecting bound IgG using an HRP-conjugated anti-human IgG secondary antibody (Genscript) and SuperSignal ELISA Femto Maxiumum Sensitivity Substrate (Thermo Scientific).
    anti-human IgG
    suggested: None
    Negative control IgGs with low polyreactivity included the human HIV-1 antibodies N664 and 3BNC11763 and bovine serum albumin (BSA).
    HIV-1
    suggested: None
    Software and Algorithms
    SentencesResources
    Movies were collected using SerialEM automated data collection software58 with beam-image shift over a 3 by 3 pattern of 1.2 µm holes with 1 exposure per hole.
    SerialEM
    suggested: (SerialEM, RRID:SCR_017293)
    In general, the data processing workflow described below was performed for all data sets in cryoSPARC v2.1559.
    cryoSPARC
    suggested: (cryoSPARC, RRID:SCR_016501)
    Structure figures were made with PyMOL (Version 1.8.2.1 Schrodinger, LLC) or UCSF ChimeraX61.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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 10.1101/2020.08.30.273920v1 on bioRxiv