Controlling the SARS-CoV-2 spike glycoprotein conformation

  1. Rory Henderson
  2. Robert J. Edwards
  3. Katayoun Mansouri
  4. Katarzyna Janowska
  5. Victoria Stalls
  6. Sophie M. C. Gobeil
  7. Megan Kopp
  8. Dapeng Li
  9. Rob Parks
  10. Allen L. Hsu
  11. Mario J. Borgnia
  12. Barton F. Haynes
  13. Priyamvada Acharya

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Abstract

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  1. Version published to 10.1038/s41594-020-0479-4
  2. ScreenIT

    SciScore for 10.1101/2020.05.18.102087: (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

    Experimental Models: Cell Lines
    SentencesResources
    This work was supported by UM1 AI100645 ( B.F . H) , the Duke Center for HIV/AIDS Vaccine ImmunologyImmunogen Discovery , and UM1 AI44371 ( B.F . H . ) , the Duke Consortium for HIV/AIDS Vaccine Development , Division of AIDS , NIAID , NIH; Duke University Center for AIDS Research ( CFAR); bioRxiv
    UM1 AI44371
    suggested: None
    Software and Algorithms
    SentencesResources
    Finally, the disposition of the CD to the inner portion of S2 measured as an angle between a vector connected to an interior S2 β-sheet motif and the vector connecting bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102087. this version posted May 18, 2020.
    bioRxiv
    suggested: (bioRxiv, SCR_003933)
    Consistent with what was observed in the NSEM analysis, after multiple rounds of 2D and 3D-classification to remove junk particles and broken and/or misfolded spikes, we found a population of ‘down’ state spike in the rS2d dataset through ab initio classification in cryoSparc.
    cryoSparc
    suggested: (cryoSPARC, SCR_016501)
    Coordinates were then fit manually in Coot45 followed by iterative refinement using Phenix46 real space refinement and subsequent manual coordinate fitting in Coot.
    Coot
    suggested: (Coot, SCR_014222)
    Structure and map analysis were performed using PyMol and Chimera. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102087. this version posted May 18, 2020.
    PyMol
    suggested: (PyMOL, SCR_000305)

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.05.18.102087v1 on bioRxiv