Engineered ACE2 receptor traps potently neutralize SARS-CoV-2

  1. Anum Glasgow
  2. Jeff Glasgow
  3. Daniel Limonta
  4. Paige Solomon
  5. Irene Lui
  6. Yang Zhang
  7. Matthew A. Nix
  8. Nicholas J. Rettko
  9. Shoshana Zha
  10. Rachel Yamin
  11. Kevin Kao
  12. Oren S. Rosenberg
  13. Jeffrey V. Ravetch
  14. Arun P. Wiita
  15. Kevin K. Leung
  16. Shion A. Lim
  17. Xin X. Zhou
  18. Tom C. Hobman
  19. Tanja Kortemme
  20. James A. Wells

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Abstract

During the ongoing COVID-19 pandemic, protein engineering offers a rapid and powerful approach for building therapeutics to treat severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We use computational design, affinity maturation, and fusion to dimerization domains to engineer “receptor traps” based on wild-type angiotensin-converting enzyme II (ACE2), the target for viral spike-mediated SARS-CoV-2 entry into cells. The optimized ACE2 receptor traps neutralize authentic SARS-CoV-2 infections as effectively as high-affinity antibodies isolated from convalescent patients and also bind viral spike proteins from other coronaviruses known to cause respiratory diseases. ACE2 receptor traps have large binding interfaces and block the entire receptor binding interface, limiting the potential impact of viral escape mutations.

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    SciScore for 10.1101/2020.07.31.231746: (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.

    Table 2: Resources

    No key resources detected.

    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.

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  2. Version published to 10.1073/pnas.2016093117
  3. Version published to 10.1101/2020.07.31.231746v3 on bioRxiv
  4. Version published to 10.1101/2020.07.31.231746v1 on bioRxiv
  5. Version published to 10.1101/2020.07.31.231746v2 on bioRxiv