Impact of Early Pandemic Stage Mutations on Molecular Dynamics of SARS-CoV‑2 Mpro

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

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Abstract

A new coronavirus (SARS-CoV-2) is a global threat to world health and economy. Its dimeric main protease (Mpro), which is required for the proteolytic cleavage of viral precursor proteins, is a good candidate for drug development owing to its conservation and the absence of a human homolog. Improving our understanding of Mpro behavior can accelerate the discovery of effective therapies to reduce mortality. All-atom molecular dynamics (MD) simulations (100 ns) of 50 mutant Mpro dimers obtained from filtered sequences from the GISAID database were analyzed using root-mean-square deviation, root-mean-square fluctuation, Rg, averaged betweenness centrality, and geometry calculations. The results showed that SARS-CoV-2 Mpro essentially behaves in a similar manner to its SAR-CoV homolog. However, we report the following new findings from the variants: (1) Residues GLY15, VAL157, and PRO184 have mutated more than once in SARS CoV-2; (2) the D48E variant has lead to a novel “TSEEMLN”” loop at the binding pocket; (3) inactive apo Mpro does not show signs of dissociation in 100 ns MD; (4) a non-canonical pose for PHE140 widens the substrate binding surface; (5) dual allosteric pockets coinciding with various stabilizing and functional components of the substrate binding pocket were found to display correlated compaction dynamics; (6) high betweenness centrality values for residues 17 and 128 in all Mpro samples suggest their high importance in dimer stabilityone such consequence has been observed for the M17I mutation whereby one of the N-fingers was highly unstable. (7) Independent coarse-grained Monte Carlo simulations suggest a relationship between the rigidity/mutability and enzymatic function. Our entire approach combining database preparation, variant retrieval, homology modeling, dynamic residue network (DRN), relevant conformation retrieval from 1-D kernel density estimates from reaction coordinates to other existing approaches of structural analysis, and data visualization within the coronaviral Mpro is also novel and is applicable to other coronaviral proteins.

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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.

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  3. Version published to 10.1101/2020.05.29.123190 on bioRxiv