Designing of a next generation multiepitope based vaccine (MEV) against SARS-COV-2: Immunoinformatics and in silico approaches

  1. Muhammad Tahir ul Qamar
  2. Abdur Rehman
  3. Kishver Tusleem
  4. Usman Ali Ashfaq
  5. Muhammad Qasim
  6. Xitong Zhu
  7. Israr Fatima
  8. Farah Shahid
  9. Ling-Ling Chen

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Abstract

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory coronavirus 2 (SARS-COV-2) is a significant threat to global health security. Till date, no completely effective drug or vaccine is available to cure COVID-19. Therefore, an effective vaccine against SARS-COV-2 is crucially needed. This study was conducted to design an effective multiepitope based vaccine (MEV) against SARS-COV-2. Seven highly antigenic proteins of SARS-COV-2 were selected as targets and different epitopes (B-cell and T-cell) were predicted. Highly antigenic and overlapping epitopes were shortlisted. Selected epitopes indicated significant interactions with the HLA-binding alleles and 99.93% coverage of the world’s population. Hence, 505 amino acids long MEV was designed by connecting 16 MHC class I and eleven MHC class II epitopes with suitable linkers and adjuvant. MEV construct was non-allergenic, antigenic, stable and flexible. Furthermore, molecular docking followed by molecular dynamics (MD) simulation analyses, demonstrated a stable and strong binding affinity of MEV with human pathogenic toll-like receptors (TLR), TLR3 and TLR8. Finally, MEV codons were optimized for its in silico cloning into Escherichia coli K-12 system, to ensure its increased expression. Designed MEV in present study could be a potential candidate for further vaccine production process against COVID-19. However, to ensure its safety and immunogenic profile, the proposed MEV needs to be experimentally validated.

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  1. Version published to 10.1371/journal.pone.0244176
  2. ScreenIT

    SciScore for 10.1101/2020.02.28.970343: (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
    To visualize the docked complex and draw figures, the PyMOL educational version was used [51].
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)
    Complexes of MEV with TLR3 and TLR8 were simulated at 20 ns using GROMACS 5.1.4 [67] by following the same protocol of our previously published studies [5, 49, 68].
    GROMACS
    suggested: (GROMACS, RRID:SCR_014565)
    Coli pET28a(+) vector with SnapGene 4.2 tool (https:/snapgene.com/) to ensure the in vitro expression.
    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 pages 20 and 21. 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.
    • 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

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  3. Version published to 10.1101/2020.02.28.970343v2 on bioRxiv
  4. Version published to 10.1101/2020.02.28.970343v1 on bioRxiv