Structure-Altering Mutations of the SARS-CoV-2 Frame Shifting RNA Element

  1. T. Schlick
  2. Q. Zhu
  3. S. Jain
  4. S. Yan

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Abstract

With the rapid rate of Covid-19 infections and deaths, treatments and cures besides hand washing, social distancing, masks, isolation, and quarantines are urgently needed. The treatments and vaccines rely on the basic biophysics of the complex viral apparatus. While proteins are serving as main drug and vaccine targets, therapeutic approaches targeting the 30,000 nucleotide RNA viral genome form important complementary approaches. Indeed, the high conservation of the viral genome, its close evolutionary relationship to other viruses, and the rise of gene editing and RNA-based vaccines all argue for a focus on the RNA agent itself. One of the key steps in the viral replication cycle inside host cells is the ribosomal frameshifting required for translation of overlapping open reading frames. The frameshifting element (FSE), one of three highly conserved regions of coronaviruses, includes an RNA pseudoknot considered essential for this ribosomal switching. In this work, we apply our graph-theory-based framework for representing RNA secondary structures, “RAG” (RNA-As Graphs), to alter key structural features of the FSE of the SARS-CoV-2 virus. Specifically, using RAG machinery of genetic algorithms for inverse folding adapted for RNA structures with pseudoknots, we computationally predict minimal mutations that destroy a structurally-important stem and/or the pseudoknot of the FSE, potentially dismantling the virus against translation of the polyproteins. Additionally, our microsecond molecular dynamics simulations of mutant structures indicate relatively stable secondary structures. These findings not only advance our computational design of RNAs containing pseudoknots; they pinpoint to key residues of the SARS-CoV-2 virus as targets for anti-viral drugs and gene editing approaches.

SIGNIFICANCE

Since the outbreak of Covid-19, numerous projects were launched to discover drugs and vaccines. Compared to protein-focused approaches, targeting the RNA genome, especially highly conserved crucial regions, can destruct the virus life cycle more fundamentally and avoid problems of viral mutations. We choose to target the small frame-shifting element (FSE) embedded in the Open Reading Frame 1a,b of SARS-CoV-2. This FSE is essential for translating overlapping reading frames and thus controlling the viral protein synthesis pathway. By applying graph-theory-based computational algorithms, we identify structurally crucial residues in the FSE as potential targets for anti-viral drugs and gene editing.

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    SciScore for 10.1101/2020.08.28.271965: (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
    Structure validation was performed by MolProbity (43) (using nuclear hydrogen positions), and the structure with least steric clashes and best geometry was selected for the subsequent MD simulations.
    MolProbity
    suggested: (MolProbity, RRID:SCR_014226)
    The trajectories were analyzed using Gromacs, and structure visualization was prepared by PyMOL (49) and VMD (50).
    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 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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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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  2. Version published to 10.1101/2020.08.28.271965v1 on bioRxiv