GlycoSHIELD: a versatile pipeline to assess glycan impact on protein structures

  1. Michael Gecht
  2. Sören von Bülow
  3. Camille Penet
  4. Gerhard Hummer
  5. Cyril Hanus
  6. Mateusz Sikora

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Abstract

More than 75% of surface and secreted proteins are modified by covalent addition of complex sugars through N- and O-glycosylation. Unlike proteins, glycans do not typically adopt specific secondary structures and remain very mobile, influencing protein dynamics and interactions with other molecules. Glycan conformational freedom impairs complete structural elucidation of glycoproteins. Computer simulations may be used to model glycan structure and dynamics. However, such simulations typically require thousands of computing hours on specialized supercomputers, thus limiting routine use. Here, we describe a reductionist method that can be implemented on personal computers to graft ensembles of realistic glycan conformers onto static protein structures in a matter of minutes. Using this open-source pipeline, we reconstructed the full glycan cover of SARS-CoV-2 Spike protein (S-protein) and a human GABAA receptor. Focusing on S-protein, we show that GlycoSHIELD recapitulates key features of extended simulations of the glycosylated protein, including epitope masking, and provides new mechanistic insights on N-glycan impact on protein structural dynamics.

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  1. Version published to 10.1101/2021.08.04.455134v3 on bioRxiv
  2. Version published to 10.1101/2021.08.04.455134v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.08.04.455134: (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
    Minimization, equilibration and production: MDS were performed with GROMACS 2018.2, 2019.6 or 2020.2 engines (11)
    GROMACS
    suggested: (GROMACS, RRID:SCR_014565)
    GlycoSHIELD: Methodology and user scripts: A library of conformers of 11 distinct glycans (up to 40 000 conformers per glycan) was generated and is publicly available on Zenodo (https://zenodo.org/record/5083355).
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)
    Currently, only single chain PDBs are supported by GlycoSASA.py Dependencies: GlycoSHIELD requires the MDAnalysis-1.1.1 (17), numpy-1.21.1 (19) and Matplotlib-2.2.3 (20) Python packages.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Structures corresponding to a range of angles were generated and linker region reconstructed using MODELLER (22).
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    3D structure rendering: Rendering of molecular structures were performed with PyMOL (23),
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: Thank you for sharing your code and 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: Please consider improving the rainbow (“jet”) colormap(s) used on page 10. 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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  4. Version published to 10.1101/2021.08.04.455134v1 on bioRxiv