Online biophysical predictions for SARS-CoV-2 proteins

  1. Luciano Kagami
  2. Joel Roca-Martínez
  3. Jose Gavaldá-García
  4. Pathmanaban Ramasamy
  5. K. Anton Feenstra
  6. Wim Vranken

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Abstract

The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. We here present a website ( http://sars2.bio2byte.be/ ) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation and protein-protein interaction propensities. These predictions attempt to capture the ‘emergent’ properties of the proteins, so the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide an indication of the biophysical variation that is observed in homologous proteins, which give an indication of the limits of the functionally relevant biophysical behaviour of these proteins. With this website, we therefore hope to provide researchers with further clues on the behaviour of SARS-CoV-2 proteins.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.04.411744: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Multiple sequence alignments (MSAs) for these sequences were obtained using a BLAST search from UniProt using default parameters against the Uniref90 protein dataset.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    This was followed by the standard UniProt ClustalW alignment procedure to obtain the MSA.
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    Phosphorylation sites: Experimentally validated phosphorylation sites were obtained from two SARS-CoV-2 phosphoproteome projects (PXD020183, PXD019113) in the PRIDE repository12.
    PRIDE
    suggested: (Pride-asap, RRID:SCR_012052)
    Website: The information was visualized online using the Django framework, with the ApexCharts JavaScript library employed for visualization of the predictions and their MSA distribution.
    Django
    suggested: (Django, RRID:SCR_012855)

    Results from OddPub: Thank you for sharing your data.

    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

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  2. Version published to 10.1101/2020.12.04.411744 on bioRxiv