Structural basis of a shared antibody response to SARS-CoV-2

  1. Meng Yuan
  2. Hejun Liu
  3. Nicholas C. Wu
  4. Chang-Chun D. Lee
  5. Xueyong Zhu
  6. Fangzhu Zhao
  7. Deli Huang
  8. Wenli Yu
  9. Yuanzi Hua
  10. Henry Tien
  11. Thomas F. Rogers
  12. Elise Landais
  13. Devin Sok
  14. Joseph G. Jardine
  15. Dennis R. Burton
  16. Ian A. Wilson

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Abstract

In the fight against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibodies are a key tool, both as potential therapeutics and to guide vaccine development. Yuan et al. focused on finding shared antibody responses, in which multiple individuals develop antibodies against the same antigen using the same genetic elements and modes of recognition. The authors identified the immunoglobulin heavy-chain variable region 3-53 gene as the most frequently used among 294 antibodies that target the receptor-binding domain (RBD) of the viral spike protein. These antibodies have few somatic mutations, and crystal structures of two neutralizing antibodies bound to the RBD show that mostly germline-encoded residues are involved in binding. The minimal affinity maturation and high potency of these antibodies is promising for vaccine design.

Science , this issue p. 1119

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  1. Version published to 10.1126/science.abd2321
  2. ScreenIT

    SciScore for 10.1101/2020.06.08.141267: (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

    Software and Algorithms
    SentencesResources
    Iterative model building and refinement were carried out in COOT (45) and PHENIX (46), respectively.
    COOT
    suggested: (Coot, RRID:SCR_014222)
    PHENIX
    suggested: (Phenix, RRID:SCR_014224)

    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.
    • 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 10.1101/2020.06.08.141267v1 on bioRxiv