Identification of peptide candidate against COVID-19 through reverse vaccinology: An immunoinformatics approach

  1. Rohit Pritam Das
  2. Manaswini Jagadeb
  3. Surya Narayan Rath

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Abstract

Novel corona virus disease 2019 (COVID-19) is emerging as a pandemic situation and declared as a global health emergency by WHO. Due to lack of specific medicine and vaccine, viral infection has gained a frightening rate and created a devastating state across the globe. Here authors have attempted to design epitope based potential peptide as a vaccine candidate using immunoinformatics approach. As of evidence from literatures, SARS-CoV-2 Spike protein is a key protein to initiate the viral infection within a host cell thus used here as a reasonable vaccine target. We have predicted a 9-mer peptide as representative of both B-cell and T-cell epitopic region along with suitable properties such as antigenic and non-allergenic. To its support, strong molecular interaction of the predicted peptide was also observed with MHC molecules and Toll Like receptors. The present study may helpful to step forward in the development of vaccine candidates against COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.01.150805: (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
    Molecular docking was performed between the predicted peptides and MHC representative structures using PatchDock web server [22, 23, 24].
    PatchDock
    suggested: (PatchDock, RRID:SCR_017589)
    The antigenicity of predicted peptides was calculated using VaxiJen tool [25] with the cut off value 0.4.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)

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

    About SciScore

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  2. Version published to 10.1101/2020.07.01.150805v1 on bioRxiv