Immuno-informatics approach for multi-epitope vaccine designing against SARS-CoV-2

  1. Souvik Banerjee
  2. Kaustav Majumder
  3. Gerardo Jose Gutierrez
  4. Debkishore Gupta
  5. Bharti Mittal

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 novel Corona Virus Disease 2019 (COVID-19) pandemic has set the fatality rates ablaze across the world. So, to combat this disease, we have designed a multi-epitope vaccine from various proteins of Severe Acute Respiratory Syndrome Corona virus 2 (SARS-CoV-2) with an immuno-informatics approach, validated in silico to be stable, non-allergic and antigenic. Cytotoxic T-cell, helper T-cell, and B-cell epitopes were computationally predicted from six conserved protein sequences among four viral strains isolated across the world. The T-cell epitopes, overlapping with the B-cell epitopes, were included in the vaccine construct to assure the humoral and cell-mediated immune response. The beta-subunit of cholera toxin was added as an adjuvant at the N-terminal of the construct to increase immunogenicity. Interferon-gamma inducing epitopes were even predicted in the vaccine. Molecular docking and binding energetics studies revealed strong interactions of the vaccine with immune-stimulatory toll-like receptors (TLR) −2, 3, 4. Molecular dynamics simulation of the vaccine ensured in vivo stability in the biological system. The immune simulation of vaccine evinced elevated immune response. The efficient translation of the vaccine in an expression vector was assured utilizing in silico cloning approach. Certainly, such a vaccine construct could reliably be effective against COVID-19.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.07.23.218529: (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
    It was done using Multiple Sequence Alignment tool: Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) which incorporates progressive approach, seeded guide trees and HHalign package for generating swift and accurate alignments for three or more input sequences16.
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    ) ProtParam implements the N-end rule to predict half-life, assigns weight value of instability to dipeptides for instability index, mole-percent as well as volumes occupied by aliphatic amino acid side chains for aliphatic index and average of hydropathy values for GRAVY score30.
    ProtParam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    Secondary structure prediction: The secondary structure predictions of vaccine was performed with PSIPRED server(http://bioinf.cs.ucl.ac.uk/psipred/).
    PSIPRED
    suggested: (PSIPRED, RRID:SCR_010246)
    Tertiary Structure prediction: The tertiary structure prediction of construct was performed with Iterative Threading Assembly Refinement (I-TASSER) on-line server (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) which utilizes hierarchal approach to cast light on protein structure.
    https://zhanglab.ccmb.med.umich.edu/I-TASSER/
    suggested: (I-TASSER, RRID:SCR_014627)
    The GalaxyRefine displays an output of five generated structure models and their respective GDT-HA, MolProbity, Clash, RMSD, Poor rotamers’ scores alongwith % favoured regions in Ramachandran plot.
    MolProbity
    suggested: (MolProbity, RRID:SCR_014226)
    Prediction of discontinuous B-cell epitopes: Discontinuous B-cell epitopes of the vaccine candidate were predicted with ElliPro web server (http://tools.iedb.org/ellipro/).
    ElliPro
    suggested: None
    The server takes PDB file input and tries to find the protein or its homologues in PDB with the protein BLAST.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    The docking was executed with ClusPro 2.0 protein-protein docking online server (https://cluspro.bu.edu/login.php).
    ClusPro
    suggested: (ClusPro, RRID:SCR_018248)
    This server prediction methods have been compiled with Python scripts and Perl.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Molecular Dynamics simulation of the vaccine construct: Molecular dynamics (MD) simulation of the vaccine construct was performed by Galaxy server49 which uses GROMACS (GROningen MAchine for Chemical Simulations) engine for running the simulation.
    Galaxy
    suggested: (Galaxy, RRID:SCR_006281)
    Ramachandran plot analysis was performed again with RAMPAGE.
    RAMPAGE
    suggested: (RAMPAGE, RRID:SCR_017590)
    This tool is amalgamated to the PRODORIC database which harbours all related data of different organisms.
    PRODORIC
    suggested: (PRODORIC, RRID:SCR_007074)
    The SnapGene software was finally used to design the recombinant plasmid.
    SnapGene
    suggested: (SnapGene, RRID:SCR_015052)

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 17. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1101/2020.07.23.218529 on bioRxiv