Effects of SARS‐CoV‐2 mutations on protein structures and intraviral protein–protein interactions

  1. Siqi Wu
  2. Chang Tian
  3. Panpan Liu
  4. Dongjie Guo
  5. Wei Zheng
  6. Xiaoqiang Huang
  7. Yang Zhang
  8. Lijun Liu

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Abstract

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) causing coronavirus disease 2019 (COVID‐19) has infected 10 millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to a change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 20 nonsynonymous mutations in the SARS‐CoV‐2 genome in which incidence rates were all ≥ 1% as of September 1st, 2020, and then modeled and analyzed the mutant protein structures. The results showed that four types of mutations caused dramatic changes in protein structures (RMSD ≥ 5.0 Å), which were Q57H and G251V in open‐reading frames 3a (ORF3a), S194L, and R203K/G204R in nucleocapsid (N). Next, we found that these mutations also affected the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking mutant complexes were altered, among which the mutation Q57H was involved in both Orf3a–S and Orf3a–Orf8 protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS‐CoV‐2 mutations could change viral protein structure, binding affinity, and hot spots of the interface, thereby might have impacts on SARS‐CoV‐2 transmission, diagnosis, and treatment of COVID‐19.

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  1. Version published to 10.1002/jmv.26597
  2. ScreenIT

    SciScore for 10.1101/2020.08.15.241349: (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
    Data were visualized by GraphPad Prism 8.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Protein structure prediction: Protein structure of control SARS-CoV-2 S was from C-I-TASSER (https://zhanglab.ccmb.med.umich.edu/C-I-TASSER/2019-nCoV/) [41], while other SARS-CoV-2 protein structure models were predicted by I-TASSER [9].
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)
    Data visualization was accomplished by PyMOL.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    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.

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  3. Version published to 10.1101/2020.08.15.241349v1 on bioRxiv