Modular basis for potent SARS-CoV-2 neutralization by a prevalent VH1-2-derived antibody class

  1. Micah Rapp
  2. Yicheng Guo
  3. Eswar R. Reddem
  4. Lihong Liu
  5. Pengfei Wang
  6. Jian Yu
  7. Gabriele Cerutti
  8. Jude Bimela
  9. Fabiana Bahna
  10. Seetha Mannepalli
  11. Baoshan Zhang
  12. Peter D. Kwong
  13. David D. Ho
  14. Lawrence Shapiro
  15. Zizhang Sheng

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Abstract

Antibodies with heavy chains that derive from the VH1-2 gene constitute some of the most potent SARS-CoV-2-neutralizing antibodies yet identified. To provide insight into whether these genetic similarities inform common modes of recognition, we determined structures of the SARS-CoV-2 spike in complex with three VH1-2-derived antibodies: 2-15, 2-43, and H4. All three utilized VH1-2-encoded motifs to recognize the receptor-binding domain (RBD), with heavy chain N53I enhancing binding and light chain tyrosines recognizing F486 RBD . Despite these similarities, class members bound both RBD-up and -down conformations of the spike, with a subset of antibodies utilizing elongated CDRH3s to recognize glycan N 343 on a neighboring RBD – a quaternary interaction accommodated by an increase in RBD separation of up to 12 Å. The VH1-2-antibody class thus utilizes modular recognition encoded by modular genetic elements to effect potent neutralization, with VH-gene component specifying recognition of RBD and CDRH3 component specifying quaternary interactions.

Highlights

  • Determine structures of VH1-2-derived antibodies 2-43, 2-15, and H4 in complex with SARS-CoV-2 spike

  • Define a multi-donor VH1-2-antibody class with modular components for RBD and quaternary recognition

  • Reveal structural basis of RBD-up and RBD-down recognition within the class

  • Show somatic hypermutations and avidity to be critical for potency

  • Delineate changes in spike conformation induced by CDRH3-mediated quaternary recognition

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  1. ScreenIT

    SciScore for 10.1101/2021.01.11.426218: (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
    Production of Fab from IgG: Fab fragments were produced from purified IgGs of monoclonal antibodies 2-15, 2-43 and H4 by digestion with Papain in the presence of the reducing agent 30 mM cysteine and were purified by affinity chromatography on protein A following the manufacturer’s protocols (Thermo fisher).
    H4
    suggested: None
    Binding Affinity Measurements by Surface Plasmon Resonance: The binding affinities of antibodies to SARS-CoV-2 spike protein were determined using surface plasmon resonance (SPR) and a BIAcore T200 instrument (GE Healthcare) at 25°C.
    SARS-CoV-2 spike protein
    suggested: None
    The anti-his antibody was first immobilized onto two different flow cells of a CM5 sensorchip (BR100030, Cyvita) surface using the His Capture Kit (28995056, Cyvita) according to the manufacturer’s protocol.
    anti-his
    suggested: None
    DMEM supplemented with anti-VSV-G antibody (I1, mouse hybridoma supernatant from CRL-2700; ATCC) was added to the infected cells and they were cultured overnight as described above.
    anti-VSV-G
    suggested: None
    I1
    suggested: None
    Calculation of angle of approach: To measure the RBD approaching angle of antibodies, we first identify shared epitope residues among the five members of the VH1-2 antibody class.
    VH1-2
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Expression vector was transiently transfected into Human Embryonic Kidney (HEK) 293 GnTI-Freestyle cells suspension culture in serum-free media (Invitrogen) using polyethyleneimine (Polysciences).
    HEK
    suggested: None
    HEK293T cells were grown to 80% confluency before transfection with pCMV3-SARS-CoV-2-spike (kindly provided by Dr. Peihui Wang, Shandong University, China) using FuGENE 6 (Promega).
    HEK293T
    suggested: None
    In brief, Vero E6 cells were seeded in a 96-well plate at a concentration of 2 × 104 cells per well.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    The resulting data were fit to a 1:1 binding model using Biacore Evaluation Software and were plotted using Graphpad (Graphpad Prism 7.01, San Diego)
    Graphpad
    suggested: (GraphPad, RRID:SCR_000306)
    The IC50 values were calculated using non-linear regression in GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    To identify somatic hypermutations, each antibody sequence was aligned to the assigned germline gene using MUSCLE v3.8.31 (Edgar, 2004)
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    Data were collected on a Titan Krios electron microscope operating at 300 kV, equipped with a Gatan K3 direct electron detector and energy filter, using the Leginon software package.
    Leginon
    suggested: (Leginon, RRID:SCR_016731)
    All processing was done using cryoSPARC v2.15.0 (Punjani et al., 2017).
    cryoSPARC
    suggested: (cryoSPARC, RRID:SCR_016501)
    The model was then fitted interactively using ISOLDE 1.0.1 (Croll, 2018) and COOT 0.8.9.2 (Emsley and Cowtan, 2004) and using real space refinement in Phenix 1.18 (Adams et al., 2004).
    Phenix
    suggested: (Phenix, RRID:SCR_014224)
    Validation was performed using Molprobity (Davis et al., 2004) and EMRinger (Barad et al., 2015).
    Molprobity
    suggested: (MolProbity, RRID:SCR_014226)
    Diffraction data were processed with XDS (Kabsch, 2010) and scaled using AIMLESS (Evans, 2006) from the CCP4 software suite (Collaborative Computational Project, 1994)
    CCP4
    suggested: (CCP4, RRID:SCR_007255)
    Manual rebuilding of the structure using COOT (Emsley et al., 2010) was alternated with refinement using Phenix refine (Afonine et al., 2012) and PDB-REDO (Joosten et al., 2014).
    COOT
    suggested: (Coot, RRID:SCR_014222)
    PISA was used to identify paratope-epitope interfaces and to calculate buried surface area (Krissinel and Henrick, 2007)
    PISA
    suggested: (PISA, RRID:SCR_015749)
    PyMOL was used to determine the centre of mass of the shared epitope residues.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: Thank you for sharing your code and 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.

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  2. Version published to 10.1101/2021.01.11.426218 on bioRxiv