Deep Mutational Engineering of broadly-neutralizing and picomolar affinity nanobodies to accommodate SARS-CoV-1 & 2 antigenic polymorphism

  1. Adrien Laroche
  2. Maria Lucia Orsini Delgado
  3. Philippe Cuniasse
  4. Steven Dubois
  5. Raphaël Sierocki
  6. Fabrice Gallais
  7. Stéphanie Debroas
  8. Laurent Bellanger
  9. Stéphanie Simon
  10. Bernard Maillère
  11. Hervé Nozach

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Abstract

We report in this study the molecular engineering of nanobodies that bind with picomolar affinity to both SARS-CoV-1 and SARS-CoV-2 Receptor Binding Domains (RBD) and are highly neutralizing. We applied Deep Mutational Engineering to VHH72, a nanobody initially specific for SARS-CoV-1 RBD with little cross-reactivity to SARS-CoV-2 antigen. We first identified all the individual VHH substitutions that increase binding to SARS-CoV-2 RBD and then screened highly focused combinatorial libraries to isolate engineered nanobodies with improved properties. The corresponding VHH-Fc molecules show high affinities for SARS-CoV-2 antigens from various emerging variants and SARS-CoV-1, block the interaction between ACE2 and RBD and neutralize the virus with high efficiency. Its rare specificity across sarbecovirus relies on its peculiar epitope outside the immunodominant regions. The engineered nanobodies share a common motif of three amino acids, which contribute to the broad specificity of recognition. These nanobodies appears as promising therapeutic candidates to fight SARS-CoV-2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.07.471597: (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
    Cells were incubated on ice for 15 minutes with anti- HA antibody (Invitrogen HA Tag Mouse anti-Tag, DyLight® 650 conjugate, Clone: 2-2.2.14; 1:100 dilution) and Streptavidin-PE (Thermo Fisher scientific; catalog number S866; 1:100 dilution).
    anti- HA
    suggested: (Claes Örvel lab; Karolinska Institute Cat# anti-MuV rabbit 144, RRID:AB_2747378)
    anti-Tag
    suggested: None
    Competitive indirect enzyme linked immunoassay (competitive ELISA): Nunc™ MaxiSorp 96-well Immuno-Plates (Thermo Fisher Scientific, Illkirch, France) were coated with 200 μL/well of AffinityPure goat anti-mouse IgG+IgM (H+L) antibody (Jackson Immuno Research Laboratories Inc., Pennsylvania, USA) at 10 μg/mL in 50 mM potassium phosphate buffer, and incubated overnight (16h) at 22 ± 2 °C.
    anti-mouse IgG+IgM
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    HEK293 Freestyle™ were transiently transfected at a density of 2.5 106 cells/mL in 100mL Freestyle medium (Thermo-Fisher) by addition of 150 μg plasmid and 1.8 mL of linear polyethylenimine (PEI, 0.5 mg/ml) (Polysciences).
    HEK293
    suggested: None
    Then, the culture medium of each plate containing the VERO E6 cells is removed and 500 μL of each VHH/virus mixture is added to each well in duplicate.
    VERO E6
    suggested: None
    Recombinant DNA
    SentencesResources
    Gap repair transformations were made in plasmid pNT VHH72 between restriction sites NheI and NotI with 1 μg of digested vector and a molar ratio of 12:1 (library/digested vector).
    pNT
    suggested: None
    Genes coding for the various RBD domains were cloned in the pCAGGS RBD-SARS-CoV-2 plasmid, which was a kind gift from Florian Krammer lab.
    pCAGGS RBD-SARS-CoV-2
    suggested: None
    Software and Algorithms
    SentencesResources
    Reads were demultiplexed and each sample was processed separately using the Galaxy platform (https://usegalaxy.org/) using the functions described in Blankenberg et al 45.
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    After seven days at 37°C, 120 rpm, 8% CO2, supernatant was purified using HiTrap Protein A for VHH-Fc constructs or HisTrap Excel for RBD constructs, following the manufacturer’s instructions (GE Healthcare).
    HisTrap Excel
    suggested: None
    Affinity measurement by BioLayer Interferometry: Binding kinetics were determined using an Octet RED96 instrument (ForteBio).
    BioLayer
    suggested: (Harvard Medical School Center for Macromolecular Interactions Core Facility, RRID:SCR_018270)
    The initial structure of VHH72 was built by homology modelling using the MODELLER software 46 and the coordinates of VHH72 in PDB structure 6WAQ as template 18.
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    Analysis of the trajectories was achieved using in-house scripts written in the macrolanguage of CHARMM v42b153.
    CHARMM
    suggested: (CHARMM, RRID:SCR_014892)
    Figures were produced with PyMol [PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.] and Gnuplot 5.1.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    Gnuplot
    suggested: (Gnuplot, RRID:SCR_008619)

    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: We detected the following sentences addressing limitations in the study:
    As a result, a second generation of antibodies might be required to overcome these limitations and could be obtained by reshaping the initial sequences by conferring them the necessary properties, such as affinity and selectivity, to have optimal therapeutic efficacy for treatment in humans. From this perspective, many teams have proposed a wide range of methods to generate candidates with the expected properties, mostly increased affinity.29-32 Affinity maturation aims at improving biological activity by adjusting the kinetic parameters of the binding to the target, which in turn may confer greater therapeutic efficacy.25,33 However, the magnitude of this effect depends largely on the epitope recognized by the antibody and the initial affinity along with the format of the antibody and its valence.25 In the context of the current COVID-19 pandemic, several studies have described affinity maturation of VHH or conventional antibodies to enhance their binding to SARS-CoV-2 antigens, by CDR-swapping approaches 34, saturation mutagenesis in CDRs 35,36 or light-chain shuffling36. In recent years, Deep Mutational Scanning (DMS) approaches have emerged as a powerful tool for understanding protein/protein interactions. Deep Mutational Scanning (DMS) explores in a selected protein all possible unique substitutions, i.e. all unique mutations for each position. DMS defines mutational landscape of the protein and helps to understand the interaction modalities as recently shown for the RBD...

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.12.07.471597v1 on bioRxiv