De novo 3D models of SARS-CoV-2 RNA elements and small-molecule-binding RNAs to aid drug discovery

  1. Ramya Rangan
  2. Andrew M. Watkins
  3. Jose Chacon
  4. Wipapat Kladwang
  5. Ivan N. Zheludev
  6. Jill Townley
  7. Mats Rynge
  8. Gregory Thain
  9. Rhiju Das

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Abstract

The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta’s FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5’ UTR; the reverse complement of the 5’ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3’ UTR. For eleven of these elements (the stems in SL1-8, reverse complement of SL1-4, FSE, s2m, and 3’ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2 ; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch ’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.

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  1. Version published to 10.1101/2020.04.14.041962v2 on bioRxiv
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    SciScore for 10.1101/2020.04.14.041962: (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
    We modeled RNA secondary structures using RNAstructure (27) guided by SHAPE or DMS reactivity data using default parameters, through MATLAB wrapper scripts available in the Biers package (https://github.com/ribokit/Biers).
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The structural ensembles presented here have a number of limitations. First, these ensembles are not true thermodynamic ensembles in that the structure occupancies do not necessarily reflect the underlying probabilities of occurrence for each conformational state. Additionally, some of the simulation ensembles described here did not achieve sufficient convergence to provide confidence in the resulting models (3’ UTR hypervariable region, extended 3’ UTR pseudoknot) – that is, independent modeling runs did not converge to similar low energy structures. It is possible that these RNA elements do not have well-defined 3D structures in solution unless bound tightly to partners such as the SARS-CoV-2 replicase complex. Alternatively, or in addition, our modeling methods and currently available computational power are not well-suited to regions of this size. While additional sampling may alleviate this problem, these regions have more de novo modeled positions than most prior FARFAR2 benchmark cases and may remain challenging for current de novo RNA modeling approaches. Given these limitations in de novo modeling, we felt that it was important to provide analogous models of RNAs of known structure. Virtual screening approaches appear poised to make good use of computational models of RNA, but have so far made only limited use of de novo predicted models (15). To provide benchmark structural ensembles for such efforts, we have therefore used the same Rosetta-FARFAR2 modeling method a...

    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 10.1101/2020.04.14.041962v1 on bioRxiv