Immunoinformatic approach to design a vaccine against SARS-COV-2 membrane glycoprotein

  1. Radhika Ravindran
  2. Shoba Gunasekaran
  3. Murugesh Easwaran
  4. Sajitha Lulu
  5. P. Ambili Unni
  6. S. Vino
  7. Mukesh Doble

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Abstract

SARS-COV-2 is a pandemic virus causing COVID-19 disease which affects lungs and upper respiratory tract leading to progressive increase in the death rate worldwide. Currently, there are more than 123 million cases and over 2.71 million confirmed death caused by this virus. In this study, by utilizing an immunoinformatic approach, multiepitope-based vaccine is designed from the membrane protein which plays a vital role in the virion assembly of the novel-CoV. A total of 19 MHC class- I binders with HLA-A and HLA-B alleles have been selected with NetMHC pan EL 4.0 method from IEDB MHC-I prediction server. Four epitopes candidates from M-protein were selected based on the antigenicity, stability, immunogenicity, Ramachandran plot and scores with 100 % was taken for docking analysis with alleles HLA-A (PDB ID: 1B0R) and HLA-B (PDB ID: 3C9N) using ClusPro server. Among the four epitopes, the epitope FVLAAVYRI has the least binding energy and forms electrostatic, hydrogen and hydrophobic interactions with HLA-A (−932.8 Kcal/mol) and HLA-B (−860.7 Kcal/mol) which induce the T-cell response. Each HLA-A and HLA-B complex in the system environment achieves stable backbone configuration between 45-100 ns of MD simulation. This study reports a potent antigenic and immunogenic profile of FVLAAVYRI epitope from M-protein and further in vitro and in vivo validation is needed for its adaptive use as vaccine against COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.26.436314: (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 flow chart summarises the methods, tools and servers used for selecting the epitopes (Fig.1). 2.1 Dataset: The sequence of membrane glycoprotein from Human SARS coronavirus (SARS-CoV) was retrieved from NCBI database and its Genbank ID was found to be QKR85720.1 (
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    The instability index of membrane protein was predicted using ProtParam tool.
    ProtParam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    RAMPAGE server was employed for the retrieval of stereochemical features of the modelled epitopes (Lovell et al. 2003).
    RAMPAGE
    suggested: (RAMPAGE, RRID:SCR_017590)
    Docking algorithm employed in ClusPro identifies the conformers with least energy and favourable surface complementarities.
    ClusPro
    suggested: (ClusPro, RRID:SCR_018248)
    2.8 Molecular dynamics study: The structural stability of both, HLA-A and B epitopes and protein complexes were calculated by Desmond software, an embedded version from Schrodinger suite.
    Desmond
    suggested: (Desmond, RRID:SCR_014575)

    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 10.1101/2021.03.26.436314v1 on bioRxiv