Impact of Early Pandemic Stage Mutations on Molecular Dynamics of SARS-CoV-2 M pro

  1. Olivier Sheik Amamuddy
  2. Gennady M. Verkhivker
  3. Özlem Tastan Bishop

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  1. Version published to 10.1021/acs.jcim.0c00634
  2. ScreenIT

    SciScore for 10.1101/2020.05.29.123190: (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 (version 2.4) [64] was used to remove any non-protein molecule and to reconstitute the biological unit as chains A and B.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    A local BLAST database was then set up for these sequences using the makeblastdb command available from the BLAST+ application (version 2.8.1) [65].
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    BLAST+
    suggested: (Japan Bioinformatics, RRID:SCR_012250)
    Homology modelling, pH adjustment and analysis of residue interactions: PIR-formatted target-template sequence alignment files were generated for each mutant using the BioPython library (Version 1.76) [66] within ad hoc Python scripts for use in MODELLER (version 9.22) [67].
    BioPython
    suggested: (Biopython, RRID:SCR_007173)
    Python
    suggested: (IPython, RRID:SCR_001658)
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    For visualising the overall interactions at given residue positions, the Arpeggio tool [69] was used to programmatically generate the inter-residue interactions, before computing their sums using an in-house Python script.
    Arpeggio
    suggested: (Arpeggio, RRID:SCR_010876)
    Molecular dynamics simulations: All-atom protein MD simulations were run for the protonated dimers using GROMACS (version 2016.1) [71] at the Center for High Performance Computing (CHPC).
    GROMACS
    suggested: (GROMACS, RRID:SCR_014565)
    The generated data was then visualised and analysed using various open source Python libraries, such as matplotlib [72], Seaborn, Pandas [73], NumPy [74], SciPy [75], MDTraj [76] and NGLview [77].
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 17 and 15. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    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 10.1101/2020.05.29.123190v1 on bioRxiv