The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins

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Abstract

Fast evolution of the SARS-CoV-2 virus provides us with unique information about the patterns of genetic changes in a single pathogen in the timescale of months. This data is used extensively to track the phylodynamic of the pandemic’s spread and its split into distinct clades. Here we show that the patterns of SARS-CoV-2 virus mutations along its genome are closely correlated with the structural features of the coded proteins. We show that the foldability of proteins’ 3D structures and conservation of their functions are the universal factors driving evolutionary selection in protein-coding genes. Insights from the analysis of mutation distribution in the context of the SARS-CoV-2 proteins’ structures and functions have practical implications including evaluating potential antigen epitopes or selection of primers for PCR-based COVID-19 tests.

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  1. SciScore for 10.1101/2020.08.10.244756: (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
    Alignment, variant calling, and annotation: We calculated a multiple sequence alignment (MSA) of all high-quality SARS-CoV-2 genomes using MAFFT version 7 (https://mafft.cbrc.jp/alignment/server/) with the default parameters.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The MSA file was then processed using SNP-sites[33] and BCFtools version 1.9[34] for variant calling and variant normalizations, respectively.
    BCFtools
    suggested: (SAMtools/BCFtools, RRID:SCR_005227)
    To annotate variants, we used SnpEff (http://snpeff.sourceforge.net/).
    SnpEff
    suggested: (SnpEff, RRID:SCR_005191)
    The collected experimental and modeled biological assemblies of SARS-CoV-2 proteins were used to calculate solvent exposure with the GetArea program[37].
    GetArea
    suggested: None

    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:
    • 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.

    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.

  2. SciScore for 10.1101/2020.08.10.244756: (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

    Antibodies
    SentencesResources
    Antibody binding and ACE2 areas were derived from separate PDB entries (Ids of PDB entries used to define epitopes and RBD-ACE2 interface are shown above the 3D insets).
    ACE2
    suggested: None
    Software and Algorithms
    SentencesResources
    Alignment, variant calling, and annotation We calculated a multiple sequence alignment (MSA) of all high-quality SARS-CoV-2 genomes using MAFFT version 7 (https://mafft.cbrc.jp/alignment/server/) with the default parameters.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    The MSA file was then processed using SNP-sites[33] and BCFtools version 1.9[34] for variant calling and variant normalizations, respectively.
    BCFtools
    suggested: (SAMtools/BCFtools, RRID:SCR_005227)
    The collected experimental and modeled biological assemblies of SARS-CoV-2 proteins were used to calculate solvent exposure with the GetArea program[37].
    GetArea
    suggested: None

    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 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.


    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.