Computational Saturation Mutagenesis of SARS-CoV-1 Spike Glycoprotein: Stability, Binding Affinity, and Comparison With SARS-CoV-2

  1. Adebiyi Sobitan
  2. Vidhyanand Mahase
  3. Raina Rhoades
  4. Dejaun Williams
  5. Dongxiao Liu
  6. Yixin Xie
  7. Lin Li
  8. Qiyi Tang
  9. Shaolei Teng

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Abstract

Severe Acute respiratory syndrome coronavirus (SARS-CoV-1) attaches to the host cell surface to initiate the interaction between the receptor-binding domain (RBD) of its spike glycoprotein (S) and the human Angiotensin-converting enzyme (hACE2) receptor. SARS-CoV-1 mutates frequently because of its RNA genome, which challenges the antiviral development. Here, we per-formed computational saturation mutagenesis of the S protein of SARS-CoV-1 to identify the residues crucial for its functions. We used the structure-based energy calculations to analyze the effects of the missense mutations on the SARS-CoV-1 S stability and the binding affinity with hACE2. The sequence and structure alignment showed similarities between the S proteins of SARS-CoV-1 and SARS-CoV-2. Interestingly, we found that target mutations of S protein amino acids generate similar effects on their stabilities between SARS-CoV-1 and SARS-CoV-2. For example, G839W of SARS-CoV-1 corresponds to G857W of SARS-CoV-2, which decrease the stability of their S glycoproteins. The viral mutation analysis of the two different SARS-CoV-1 isolates showed that mutations, T487S and L472P, weakened the S-hACE2 binding of the 2003–2004 SARS-CoV-1 isolate. In addition, the mutations of L472P and F360S destabilized the 2003–2004 viral isolate. We further predicted that many mutations on N-linked glycosylation sites would increase the stability of the S glycoprotein. Our results can be of therapeutic importance in the design of antivirals or vaccines against SARS-CoV-1 and SARS-CoV-2.

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  1. Version published to 10.3389/fmolb.2021.784303
  2. ScreenIT

    SciScore for 10.1101/2021.06.30.450547: (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

    Software and Algorithms
    SentencesResources
    PyMOL (39) was used for the visualization of the PDB structures and for structural alignments.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    For each mutation, we used FoldX to calculate the folding energy change (ΔΔG) and binding energy change (ΔΔΔG) (40).
    FoldX
    suggested: (FoldX, RRID:SCR_008522)
    Mutation pathogenicity and sequence-based Analysis: We used the Polymorphism Phenotyping v2 (PolyPhen2) (41) and Screening for non-acceptable polymorphisms (SNAP) (31) prediction tools to predict the pathogenicity of each missense mutations.
    PolyPhen2
    suggested: None
    Specifically, we constructed boxplots to compare the prediction of pathogenicity between PolyPhen2 and SNAP.
    SNAP
    suggested: (SNAP, RRID:SCR_007936)
    Sequence and Structural similarity between SARS-CoV-1 and SARS-CoV-2: The FASTA sequences of SARS-CoV-1 and SARS-CoV-2 S proteins were retrieved from the Universal protein Knowledgebase (UniProtKB) (42).
    UniProtKB
    suggested: (UniProtKB, RRID:SCR_004426)
    We performed the pairwise sequence alignment of SARS-CoV-1 (Entry: P59594) and SARS-CoV-2 (Entry: P0DTC2) using the Clustal Omega computer program (https://www.ebi.ac.uk/Tools/msa/clustalo/) and Jalview2 (www.jalview.org).
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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

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  3. Version published to 10.1101/2021.06.30.450547v1 on bioRxiv