When Darkness Becomes a Ray of Light in the Dark Times: Understanding the COVID-19 via the Comparative Analysis of the Dark Proteomes of SARS-CoV-2, Human SARS and Bat SARS-Like Coronaviruses

  1. Rajanish Giri
  2. Taniya Bhardwaj
  3. Meenakshi Shegane
  4. Bhuvaneshwari R. Gehi
  5. Prateek Kumar
  6. Kundlik Gadhave
  7. Christopher J. Oldfield
  8. Vladimir N. Uversky

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Abstract

Recently emerged coronavirus designated as SARS-CoV-2 (also known as 2019 novel coronavirus (2019-nCoV) or Wuhan coronavirus) is a causative agent of coronavirus disease 2019 (COVID-19), which is rapidly spreading throughout the world now. More than 9,00,000 cases of SARS-CoV-2 infection and more than 47,000 COVID-19-associated mortalities have been reported worldwide till the writing of this article, and these numbers are increasing every passing hour. World Health Organization (WHO) has declared the SARS-CoV-2 spread as a global public health emergency and admitted that the COVID-19 is a pandemic now. The multiple sequence alignment data correlated with the already published reports on the SARS-CoV-2 evolution and indicated that this virus is closely related to the bat Severe Acute Respiratory Syndrome-like coronavirus (bat SARS-like CoV) and the well-studied Human SARS coronavirus (SARS CoV). The disordered regions in viral proteins are associated with the viral infectivity and pathogenicity. Therefore, in this study, we have exploited a set of complementary computational approaches to examine the dark proteomes of SARS-CoV-2, bat SARS-like, and human SARS CoVs by analysing the prevalence of intrinsic disorder in their proteins. According to our findings, SARS-CoV-2 proteome contains very significant levels of structural order. In fact, except for Nucleocapsid, Nsp8, and ORF6, the vast majority of SARS-CoV-2 proteins are mostly ordered proteins containing less intrinsically disordered protein regions (IDPRs). However, IDPRs found in SARS-CoV-2 proteins are functionally important. For example, cleavage sites in its replicase 1ab polyprotein are found to be highly disordered, and almost all SARS-CoV-2 proteins were shown to contain molecular recognition features (MoRFs), which are intrinsic disorder-based protein-protein interaction sites that are commonly utilized by proteins for interaction with specific partners. The results of our extensive investigation of the dark side of the SARS-CoV-2 proteome will have important implications for the structural and non-structural biology of SARS or SARS-like coronaviruses.

Significance

The infection caused by a novel coronavirus (SARS-CoV-2) that causes severe respiratory disease with pneumonia-like symptoms in humans is responsible for the current COVID-19 pandemic. No in-depth information on structures and functions of SARS-CoV-2 proteins is currently available in the public domain, and no effective anti-viral drugs and/or vaccines are designed for the treatment of this infection. Our study provides the first comparative analysis of the order- and disorder-based features of the SARS-CoV-2 proteome relative to human SARS and bat CoV that may be useful for structure-based drug discovery.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.13.990598: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    Molecular recognition features (MoRFs) determination in CoV proteomes: The authentic online bioinformatics predictors that use a different set of algorithms for the prediction of MoRFs were used: These include MoRFchibi_web [38], ANCHOR [39,40], MoRFPred [41], and DISOPRED3 [42].
    MoRFchibi_web
    suggested: None
    Software and Algorithms
    SentencesResources
    We have used Clustal Omega [27] for protein sequence alignment and Esprit 3.0 [
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    Esprit
    suggested: (ESPRIT, RRID:SCR_000552)

    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: 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. Version published to 10.1101/2020.03.13.990598 on bioRxiv