Open Modification Searching of SARS-CoV-2–Human Protein Interaction Data Reveals Novel Viral Modification Sites

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Abstract

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  1. SciScore for 10.1101/2022.03.10.483652: (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: Cell Lines
    SentencesResources
    In brief, affinity purification was performed using 27 SARS-CoV-2 proteins that were individually tagged and expressed in triplicate in HEK-293T cells.
    HEK-293T
    suggested: None
    Software and Algorithms
    SentencesResources
    SARS-CoV-2 virus-host interactome data origin: A previously generated AP-MS dataset to study the SARS-CoV-2 virus–host interactome [2] was retrieved from the PRIDE repository (PXD018117) [15].
    PRIDE
    suggested: (Pride-asap, RRID:SCR_012052)
    This is a comprehensive human HCD spectral library containing 2,154,269 unique precursors corresponding to 1,114,503 unique peptides, derived from publicly available mass spectrometry data in the MassIVE repository [17].
    MassIVE
    suggested: None
    SARS-CoV-2 spectra were simulated by generating all possible tryptic peptide sequences from the SARS-CoV-2 protein sequences downloaded from UniProt (version 2020/03/05) using Pyteomics (version 4.3.2) [18] and predicting the corresponding spectra using Prosit (version prosit_intensity_2020_hcd; collision energy 33 as determined by Prosit collision energy calibration) [19].
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    To validate the filtered PPIs, a list of SARS-CoV-2–human interactions reported in three BioID studies [25-27] and three other AP-MS studies [7-9] were obtained from the BioGRID repository [28].
    BioGRID
    suggested: (BioGrid Australia, RRID:SCR_006334)
    Gene ontology enrichment analysis: Gene Ontology (GO) enrichment analysis was performed for the human proteins that interact with each viral protein, using the enrichGO function of the clusterProfiler package (version 4.0.5) in R.
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)
    PSMs that included specific PTMs (phosphorylation, ubiquitination, and S-nitrosylation) were manually investigated in more detail to disambiguate between alternative PTM assignments with near-identical mass and determine the modification site by visual inspection using the spectrum_utils Python package (version 0.3.3) [32].
    Python
    suggested: (IPython, RRID:SCR_001658)
    Phosphorylation site prediction was performed using NetPhos (version 3.1) [36, 37].
    NetPhos
    suggested: (NetPhos, RRID:SCR_017975)

    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: 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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


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