Stabilization of the SARS-CoV-2 receptor binding domain by protein core redesign and deep mutational scanning

  1. Alison C Leonard
  2. Jonathan J Weinstein
  3. Paul J Steiner
  4. Annette H Erbse
  5. Sarel J Fleishman
  6. Timothy A Whitehead

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Abstract

Stabilizing antigenic proteins as vaccine immunogens or diagnostic reagents is a stringent case of protein engineering and design as the exterior surface must maintain recognition by receptor(s) and antigen—specific antibodies at multiple distinct epitopes. This is a challenge, as stability enhancing mutations must be focused on the protein core, whereas successful computational stabilization algorithms typically select mutations at solvent-facing positions. In this study, we report the stabilization of SARS-CoV-2 Wuhan Hu-1 Spike receptor binding domain using a combination of deep mutational scanning and computational design, including the FuncLib algorithm. Our most successful design encodes I358F, Y365W, T430I, and I513L receptor binding domain mutations, maintains recognition by the receptor ACE2 and a panel of different anti-receptor binding domain monoclonal antibodies, is between 1 and 2°C more thermally stable than the original receptor binding domain using a thermal shift assay, and is less proteolytically sensitive to chymotrypsin and thermolysin than the original receptor binding domain. Our approach could be applied to the computational stabilization of a wide range of proteins without requiring detailed knowledge of active sites or binding epitopes. We envision that this strategy may be particularly powerful for cases when there are multiple or unknown binding sites.

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  1. Version published to 10.1093/protein/gzac002
  2. ScreenIT

    SciScore for 10.1101/2021.11.22.469552: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The anti-SARS-CoV-2 RBD antibody panel used (CC6.29, CC6.32, CC6.33, CC12.1, CC12.7) was a kind gift from Dennis Burton’s lab at Scripps and were produced and purified according to Rogers et al. (Rogers et al. 2020).
    anti-SARS-CoV-2 RBD
    suggested: None
    Detection was enabled by addition of 100 µL Goat anti-Human IgG Fc secondary antibody conjugated to horseradish peroxidase (ThermoFisher #A18817), diluted 1:50,000 into PBSM and incubated in the above plate shaker for 1 h at room temperature.
    anti-Human IgG
    suggested: (Thermo Fisher Scientific Cat# A18817, RRID:AB_2535594)
    Experimental Models: Organisms/Strains
    SentencesResources
    Protein was quantified by absorbance at 280 nm using the theoretical extinction coefficient derived from the protein sequence when all four disulfide bonds are intact (WT: 33850 M-1cm-1, RBD6: 37860 M-1cm-1, RBD8: 37860 M-1cm-1, RBD10: 36370 M-1cm-1).
    M-1cm-1
    suggested: None
    Recombinant DNA
    SentencesResources
    To construct plasmids pACL002-pACL006 and pACL009-pACL015 containing all RBD design sequences for yeast surface display, DNA sequences were ordered as gBlocks (IDT) and cloned into pETconV4 using restriction enzymes NdeI and XhoI (NEB).
    pACL002-pACL006
    suggested: None
    pACL009-pACL015
    suggested: None
    To construct plasmid pACL007, the wild-type RBD sequence was amplified from plasmid pJS699 using primers forward_pJS699_RBD_pETconV4 and reverse_pJS699_RBD_pETconV4 to add regions of pETconV4 homology surrounding the RBD sequence.
    pACL007
    suggested: None
    pJS699
    suggested: RRID:Addgene_168779)
    DNA sequences for plasmids pACL002-pACL006 and Design1_pETconV4-Design7_pETconV4 containing all RBD design sequences were ordered as gBlocks (IDT) and cloned into pETconV4 using restriction enzymes NdeI and XhoI (NEB).
    pETconV4
    suggested: None
    Plasmid pACL008 was created by inserting the BbvCI restriction enzyme site into the pACL005 plasmid using site-directed mutagenesis.
    pACL005
    suggested: None
    RBD designs to be tested further as soluble proteins were codon optimized for P. pastoris (IDT), ordered as gBlocks, and cloned into the pPICZαA secreted expression vector (ThermoFisher V19520) using EcoRI and SacII restriction sites.
    pPICZαA
    suggested: None
    RBD designs were produced recombinantly in Pichia pastoris as follows. pPICZα vectors (ThermoFisher V19520) containing WT RBD or RBD designs were linearized by SacI and greater than 5 μl were transformed into electrocompetent P. pastoris X-33 (ThermoFisher C18000) at 2000V using a 2 mm electroporation cuvette (Bulldog Bio) and Eppendorf electroporator and then plated on yeast extract peptone dextrose plus sorbitol plates (YPDS: 1% w/v yeast extract, 2% w/v peptone, 2% v/v glucose, plus 1.0M sorbitol) supplemented with 100 μg/ml zeocin (ThermoFisher 25001).
    pPICZα
    suggested: RRID:Addgene_78171)
    These 90 positions were mutated to every other amino acid plus stop codon by comprehensive nicking mutagenesis (Wrenbeck et al. 2016) using NNK primers (Table S2) and template plasmid pACL008.
    pACL008
    suggested: None
    Software and Algorithms
    SentencesResources
    RBD designs to be tested further as soluble proteins were codon optimized for P. pastoris (IDT), ordered as gBlocks, and cloned into the pPICZαA secreted expression vector (ThermoFisher V19520) using EcoRI and SacII restriction sites.
    gBlocks
    suggested: (Gblocks, RRID:SCR_015945)
    Deep sequencing analysis: All deep sequencing data analysis was performed using the Protein Analysis and Classifier Toolkit (Klesmith and Hackel 2019) available at GITHUB (https://github.com/JKlesmith/PACT).
    GITHUB
    suggested: (GitHub, RRID:SCR_002630)
    The KD,app for each reaction was calculated using non-linear least squares regression performed using custom Python scripts.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Gel bands were quantified using ImageJ software (Abramoff et al. 2004) to determine the relative density of each band, compared to the average of 3 control (no thermolysin) samples on the same gel.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)

    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 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.

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

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  3. Version published to 10.1101/2021.11.22.469552v1 on bioRxiv