An Immunoinformatics Study to Predict Epitopes in the Envelope Protein of SARS-CoV-2

  1. Renu Jakhar
  2. S. K. Gakhar

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Abstract

COVID-19 is a new viral emergent disease caused by a novel strain of coronavirus. This virus has caused a huge problem in the world as millions of people are affected by this disease. We aimed at designing a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system. The envelope protein sequence of SARS-CoV-2 has been retrieved from the NCBI database. The bioinformatics analysis was carried out by using the Immune Epitope Database (IEDB) to predict B- and T-cell epitopes. The predicted HTL and CTL epitopes were docked with HLA alleles and binding energies were evaluated. The allergenicity of predicted epitopes was analyzed, the conservancy analysis was performed, and the population coverage was determined throughout the world. Some overlapped CTL, HTL, and B-cell epitopes were suggested to become a universal candidate for peptide-based vaccine against COVID-19. This vaccine peptide could simultaneously elicit humoral and cell-mediated immune responses. We hope to confirm our findings by adding complementary steps of both in vitro and in vivo studies to support this new universal predicted candidate.

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  1. Version published to 10.1155/2020/7079356
  2. ScreenIT

    SciScore for 10.1101/2020.05.26.115790: (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 antigenicity of this sequence was predicted by the VaxiJen v2.0 server [25] with default parameter.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    A total of 370 envelope protein sequences were retrieved from the NCBI database till 12 April 2020.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Patchdock program was used for all dockings [49].
    Patchdock
    suggested: (PatchDock, RRID:SCR_017589)
    PyMol and CHIMERA were used for visualization and determination of binding affinity and to show the suitable epitopes binding with the lowest energy.
    PyMol
    suggested: (PyMOL, RRID:SCR_000305)

    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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  3. Version published to 10.1101/2020.05.26.115790v1 on bioRxiv