The Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614 Mutant: Conformational Plasticity and Frustration-Driven Allostery as Energetic Drivers of Highly Transmissible Spike Variant

  1. Gennady Verkhivker
  2. Steve Agajanian
  3. Ryan Kassab
  4. Keerthi Krishnan

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Abstract

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern(VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model which can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the inter-protomer hinge modulate the inter-domain interactions, global mobility change and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the inter-protomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

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    SciScore for 10.1101/2021.12.09.471953: (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 performed principal component analysis (PCA) of reconstructed trajectories derived from CABS-CG simulations using the CARMA package110 and also determined the essential dynamics profiles in slow modes using elastic network models (ENM) analysis.111 Two elastic network models: Gaussian network model (GNM)112, 113 and Anisotropic network model (ANM) approaches114 were adopted to compute the amplitudes of isotropic thermal motions and directionality of anisotropic motions.
    CARMA
    suggested: (CARMA, RRID:SCR_004999)
    Alanine scanning of protein residues was performed using FoldX approach.
    FoldX
    suggested: (FoldX, RRID:SCR_008522)
    130 The g_correlation tool in the Gromacs 3.3 package was used that allows computation of both linear or non-linear generalized correlation coefficients.
    Gromacs
    suggested: (GROMACS, RRID:SCR_014565)
    138 Network graph calculations were performed using the python package NetworkX.139 The Girvan-Newman algorithm140–142 is used to identify local communities.
    python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on pages 80, 81, 75 and 44. 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.
    • 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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.12.09.471953v1 on bioRxiv