In silico approach for designing of a multi-epitope based vaccine against novel Coronavirus (SARS-COV-2)

  1. Ratnadeep Saha
  2. Burra V L S Prasad

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

A novel Coronavirus (SARS-COV-2) has now become a global pandemic. Considering the severity of infection and the associated mortalities, there is an urgent need to develop an effective preventive measure against this virus. In this study, we have designed a novel vaccine construct using computational strategies. Spike (S) glycoprotein is the major antigenic component that trigger the host immune responses. Detailed investigation of S protein with various immunoinformatics tools enabled us to identify 5 MHC I and 5 MHC II B-cell derived T-cell epitopes with VaxiJen score > 1 and IC 50 value < 100nM. These epitopes were joined with a suitable adjuvant and appropriate linkers to form a multi-epitope based vaccine construct. Further, in silico testing of the vaccine construct for its antigenicity, allergenicity, solubility, and other physicochemical properties showed it to be safe and immunogenic. Suitable tertiary structure of the vaccine protein was generated using 3Dpro of SCRATCH suite, refined with GalaxyRefine, and validated with ProSA, PROCHECK, and ERRAT server. Finally, molecular docking studies were performed to ensure a favorable binding affinity between the vaccine construct and TLR3 receptor. The designed multi-epitope vaccine showed potential to elicit specific immune responses against the SARS-COV-2. However, further wet lab validation is necessary to confirm the actual effectiveness, safety and immunogenic potency of the vaccine construct against derived in this study.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.31.017459: (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
    Whereas, ProPred utilizes quantitative matrices derived from the published literature (Singh and Raghava, 2001).
    ProPred
    suggested: None
    Evaluation antigenicity, allergenicity, solubility, and physicochemical properties: Antigenicity of the final vaccine construct was evaluated by using VaxiJen v.2.0.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    Furthermore, the various physicochemical parameters of the construct were assessed using ProtParam server (Wilkins et al., 1999).
    ProtParam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    Later on the structural refinement of the modelled vaccine construct was performed through GalaxyRefine web server (Heo et al., 2013).
    GalaxyRefine
    suggested: (GalaxyRefine, RRID:SCR_018531)
    Refined model was finally validated to identify any potential errors using ProSA-web
    ProSA-web
    suggested: (ProSA-web, RRID:SCR_018540)
    Conformational B-cell epitope prediction of vaccine construct: DiscoTope 2.0 tool of IEDB server was used to determine conformational B-cell epitopes by using validated 3D structure of vaccine construct as an input.
    DiscoTope
    suggested: (DiscoTope, RRID:SCR_018530)
    Finally, the binding affinity between the TLR3 receptor and vaccine construct was calculated by using ClusPro 2.0 server (Kozakov et al., 2017).
    ClusPro
    suggested: (ClusPro, RRID:SCR_018248)
    Visualization and interaction analysis of the docked complex were performed using the Chimera v1.14 and DIMPLOT program of the LigPlot+ v.2.1, respectively.
    Chimera
    suggested: (Chimera, RRID:SCR_002959)
    LigPlot+
    suggested: (LigPlot+, RRID:SCR_018249)

    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 11. 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 10.1101/2020.03.31.017459 on bioRxiv