COVID-3D: An online resource to explore the structural distribution of genetic variation in SARS-CoV-2 and its implication on therapeutic development

  1. Stephanie Portelli
  2. Moshe Olshansky
  3. Carlos H.M. Rodrigues
  4. Elston N. D’Souza
  5. Yoochan Myung
  6. Michael Silk
  7. Azadeh Alavi
  8. Douglas E.V. Pires
  9. David B. Ascher

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Abstract

The emergence of the COVID-19 pandemic has spurred a global rush to uncover basic biological mechanisms, to inform effective vaccine and drug development. Despite viral novelty, global sequencing efforts have already identified genomic variation across isolates. To enable easy exploration and spatial visualization of the potential implications of SARS-CoV-2 mutations on infection, host immunity and drug development we have developed COVID-3D ( http://biosig.unimelb.edu.au/covid3d/ ).

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  1. ScreenIT

    SciScore for 10.1101/2020.05.29.124610: (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
    Sequences were aligned using blastn to the reference genome (NC_045512.2), and synonymous and missense variants for each mature protein curated.
    blastn
    suggested: (BLASTN, RRID:SCR_001598)
    Population specific variants were jointly called using PLINK and BCFTools, and all variants were converted to GRCh38/Hg38 genomic coordinate positions.
    PLINK
    suggested: (PLINK, RRID:SCR_001757)
    Homology models were generated using Modeller28 and I-TASSER29 and optimized using Maestro.
    Maestro
    suggested: (Maestro, RRID:SCR_016748)
    Structures were validated using Meastro Protein Preparation Wizard and Molprobity.
    Molprobity
    suggested: (MolProbity, RRID:SCR_014226)
    Structural characterisation: Potential linear and structural epitopes predicted using DiscoTope 2.015 and ElliPro16 respectively, pockets detected using GHECOM14, and fragment-binding hot-spot potentials using CCDC13.
    DiscoTope
    suggested: (DiscoTope, RRID:SCR_018530)
    The Materializecss framework version 1.0.0 was used to develop the server front end, while the back-end was built in Python using the Flask framework version 1.0.2.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 10.1101/2020.05.29.124610 on bioRxiv