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 human disease caused by a novel strain of Coronavirus. This virus has caused a huge problem in the world as millions of the people are affected with this disease in the entire world. We aimed to design a peptide vaccine for COVID-19 particularly for the envelope protein using computational methods to predict epitopes inducing the immune system and can be used later to create a new peptide vaccine that could replace conventional vaccines. A total of available 370 sequences of SARS-CoV-2 were retrieved from NCBI for bioinformatics analysis using Immune Epitope Data Base (IEDB) to predict B and T cells epitopes. Then we docked the best predicted CTL epitopes with HLA alleles. CTL cell epitopes namely interacted with MHC class I alleles and we suggested them to become universal peptides based vaccine against COVID-19. Potentially continuous B cell epitopes were predicted using tools from IEDB. The Allergenicity of predicted epitopes was analyzed by AllerTOP tool and the coverage was determined throughout the worlds. We found these CTL epitopes to be T helper epitopes also. The B cell epitope, SRVKNL and T cell epitope, FLAFVVFLL were suggested to become a universal candidate for peptide-based vaccine against COVID-19. 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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    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.26.115790 on bioRxiv