Epitope-based peptide vaccine design and target site characterization against novel coronavirus disease caused by SARS-CoV-2

  1. Lin Li
  2. Ting Sun
  3. Yufei He
  4. Wendong Li
  5. Yubo Fan
  6. Jing Zhang

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The outbreak of the 2019 novel coronavirus (SARS-CoV-2) has infected thousands of people with a large number of deaths across 26 countries. The sudden appearance of the virus leads to the limited existing therapies for SARS-CoV-2. Therefore, vaccines and antiviral medicines are in desperate need. This study took immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein (S) of SARS-CoV-2, followed by estimating their antigenicity and interactions with the human leukocyte antigen (HLA) alleles. We identified four B cell epitopes, two MHC class-I and nine MHC class-II binding T-cell epitopes, which showed highly antigenic features. Allergenicity, toxicity and physiochemical properties analysis confirmed the specificity and selectivity of epitopes. The stability and safety of epitopes were confirmed by digestion analysis. No mutations were observed in all the selected B- and T-cell epitopes across all isolates from different locations worldwide. Epitopes were thus identified and some of them can be potential candidates for vaccine development.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.02.25.965434: (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
    Data retrieval and structural analysis: Primary sequence of SARS-CoV-2 protein was retrieved from NCBI database using accession number MN908947.3 [3].
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Protein sequence was analyzed for its chemicals and physical properties including GRAVY (Grand average of hydropathicity), half-life, molecular weight, stability index and amino acid atomic composition via an online tool Protparam [13]
    Protparam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    TMHMM v2.0 (http://www.cbs.dtu.dk/services/TMHMM/) was applied to examine the transmembrane topology of S protein.
    http://www.cbs.dtu.dk/services/TMHMM/
    suggested: (TMHMM Server, RRID:SCR_014935)
    Existence of disulphide-bonds were examined through an online tool DIANNA v1.1 which uses trained neural system to make predictions [15].
    DIANNA
    suggested: (DiANNA, RRID:SCR_018529)
    Pymol was used to examine the positions of selected linear and discontinuous epitopes on the 3D structure of SARS-CoV-2 protein.
    Pymol
    suggested: (PyMOL, RRID:SCR_000305)
    The antigenicity score of each epitope was calculated by VaxiJen v2.0.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    The phylogenetic tree of the S proteins with mutations were generated using MEGA software [23].
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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 10.1101/2020.02.25.965434 on bioRxiv