Structural Basis for SARS-CoV-2 Nucleocapsid Protein Recognition by Single-Domain Antibodies

  1. Qiaozhen Ye
  2. Shan Lu
  3. Kevin D. Corbett

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Abstract

The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, is the most severe public health event of the twenty-first century. While effective vaccines against SARS-CoV-2 have been developed, there remains an urgent need for diagnostics to quickly and accurately detect infections. Antigen tests, particularly those that detect the abundant SARS-CoV-2 Nucleocapsid protein, are a proven method for detecting active SARS-CoV-2 infections. Here we report high-resolution crystal structures of three llama-derived single-domain antibodies that bind the SARS-CoV-2 Nucleocapsid protein with high affinity. Each antibody recognizes a specific folded domain of the protein, with two antibodies recognizing the N-terminal RNA binding domain and one recognizing the C-terminal dimerization domain. The two antibodies that recognize the RNA binding domain affect both RNA binding affinity and RNA-mediated phase separation of the Nucleocapsid protein. All three antibodies recognize highly conserved surfaces on the Nucleocapsid protein, suggesting that they could be used to develop affordable diagnostic tests to detect all circulating SARS-CoV-2 variants.

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  1. Version published to 10.3389/fimmu.2021.719037
  2. ScreenIT

    SciScore for 10.1101/2021.06.01.446591: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: >UTR265 (nt 1-265 of SARS-CoV-2 genome) AUUAAAGGUUUAUACCUUCCCAGGUAACAAACCAACCAACUUUCGAUCUCUUGUAGAUCUGUUCUCUAAACGAACUUUAAAAUCU GUGUGGCUGUCACUCGGCUGCAUGCUUAGUGCACUCACGCAGUAUAAUUAAUAACUAAUUACUGUCGUUGACAGGACACGAGUAA CUCGUCUAUCUUCUGCAGGCUGCUUACGGUUUCGUCCGUGUUGCAGCCGAUCAUCAGCACAUCUAGGUUUCGUCCGGGUGUGACC GAAAGGUAAG Beamline support statements: This work is based upon research conducted at the Northeastern Collaborative Access Team beamlines at the Advanced Photon Source, which are funded by the National Institute of General Medical Sciences from the National Institutes of Health (P30 GM124165).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    For sdAb-N3, a codon-optimized gene was synthesized (Integrated DNA Technologies) and inserted into pET22b.
    pET22b
    suggested: RRID:Addgene_84863)
    Software and Algorithms
    SentencesResources
    10006625; NCBI RefSeq YP_009724397) and inserted by ligation-independent cloning into UC Berkeley Macrolab vectors 2B-T (AmpR, N-terminal His6-fusion; Addgene #29666) or 2C-T (AmpR, N-terminal His6-MBP fusion; Addgene #29706).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Data was automatically indexed and reduced by the RAPD data-processing pipeline (https://github.com/RAPD/RAPD), which uses XDS (Kabsch, 2010) for indexing and integration, and the CCP4 programs AIMLESS and TRUNCATE (Winn et al., 2011) for scaling and structure-factor calculation.
    CCP4
    suggested: (CCP4, RRID:SCR_007255)
    The model, comprising three identical copies of a 1:1 N49-174:sdAb-C2 complex, was manually rebuilt in COOT and refined with phenix.refine (Table S2).
    COOT
    suggested: (Coot, RRID:SCR_014222)
    Highly-mutated positions were graphed in Prism v.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    9 (Graphpad Software).
    Graphpad
    suggested: (GraphPad, RRID:SCR_000306)
    Analysis of sdAb-N interfaces was performed with PDBePISA (https://www.ebi.ac.uk/pdbe/pisa/), and graphed alongside N protein variation data.
    https://www.ebi.ac.uk/pdbe/pisa/
    suggested: (PISA, RRID:SCR_015749)
    Bound/unbound fractions were calculated in ImageJ (Schneider et al., 2012) and binding curves were calculated in Prism v.
    ImageJ
    suggested: (ImageJ, RRID:SCR_003070)

    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.

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

    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 10.1101/2021.06.01.446591v1 on bioRxiv