Glycoinformatics approach for identifying target positions to inhibit initial binding of SARS-CoV-2 S1 protein to the host cell

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Abstract

COVID-19 outbreak is still threatening the public health. Therefore, in the middle of the pandemic, all kind of knowledge on SARS-CoV-2 may help us to find the solution. Determining the 3D structures of the proteins involved in host-pathogen interactions are of great importance in the fight against infection. Besides, post-translational modifications of the protein on 3D structure should be revealed in order to understand the protein function since these modifications are responsible for the host-pathogen interaction. Based on these, we predicted O-glycosylation and phosphorylation positions using full amino acid sequence of S1 protein. Candidate positions were further analyzed with enzyme binding activity, solvent accessibility, surface area parameters and the positions determined with high accuracy rate were used to design full 3D glycoprotein structure of the S1 protein using carbohydrate force field. In addition, the interaction between the C-type lectin CD209L and α-mannose residues was examined and carbohydrate recognition positions were predicted. We suggest these positions as a potential target for the inhibition of the initial binding of SARS-CoV-2 S1 protein to the host cell.

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  1. SciScore for 10.1101/2020.03.25.007898: (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
    The potential O-glycosylation (O-GalNAc) and O-β-GlcNAc sites of S1 protein were analysed via NetOGlyc 4.0 [26] and YinOYang 1.2 Server [27], respectively.
    NetOGlyc
    suggested: (NetOGlyc, RRID:SCR_009026)
    YinOYang
    suggested: (YinOYang, RRID:SCR_001605)
    The potential phosphorylation positions of S1 protein was analysed with NetPhos 3.1 Server [30].
    NetPhos
    suggested: (NetPhos, RRID:SCR_017975)
    The structure model has been generated by the C-I-TASSER pipeline, which utilizes deep convolutional neural-network based contact-map predictions to guide the I-TASSER fragment assembly simulations.
    C-I-TASSER
    suggested: None
    CD209L lectin 3D structure (1.4 Å resolution) was taken from PDB (ID: 1XPH) [37] and α-D-mannose structure was taken from Glyco3D database [38].
    Glyco3D
    suggested: (Glyco3D, RRID:SCR_003797)
    All structures were visualized with PyMOL.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

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

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