Design of multi epitope-based peptide vaccine against E protein of human COVID-19: An immunoinformatics approach

  1. Miyssa I. Abdelmageed
  2. Abdelrahman H. Abdelmoneim
  3. Mujahed I. Mustafa
  4. Nafisa M. Elfadol
  5. Naseem S. Murshed
  6. Shaza W. Shantier
  7. Abdelrafie M. Makhawi

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Abstract

Background

New endemic disease has been spread across Wuhan City, China on December 2019. Within few weeks, the World Health Organization (WHO) announced a novel coronavirus designated as coronavirus disease 2019 (COVID-19). In late January 2020, WHO declared the outbreak of a “public-health emergency of international concern” due to the rapid and increasing spread of the disease worldwide. Currently, there is no vaccine or approved treatment for this emerging infection; thus the objective of this study is to design a multi epitope peptide vaccine against COVID-19 using immunoinformatics approach.

Method

Several techniques facilitating the combination of immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T cell epitopes-based peptide vaccine using the envelope protein of 2019-nCoV as a target.

Results

Extensive mutations, insertion and deletion were discovered with comparative sequencing in COVID-19 strain. Additionally, ten peptides binding to MHC class I and MHC class II were found to be promising candidates for vaccine design with adequate world population coverage of 88.5% and 99.99%, respectively.

Conclusion

T cell epitopes-based peptide vaccine was designed for COVID-19 using envelope protein as an immunogenic target. Nevertheless, the proposed vaccine is rapidly needed to be validated clinically in order to ensure its safety, immunogenic profile and to help on stopping this epidemic before it leads to devastating global outbreaks.

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  1. ScreenIT

    SciScore for 10.1101/2020.02.04.934232: (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
    (https://www.ncbi.nlm.nih.gov/) 2.2.
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    The sequences of E protein were retrieved from UniProt were run in BioEdit to determine the conserved sites through ClustalW in the application settings [37]. 2.5. The Molecular Evolutionary Genetics Analysis (MEGA): MEGA (version 10.1.6) is software for the comparative analysis of molecular sequences.
    UniProt
    suggested: (UniProtKB, RRID:SCR_004426)
    BioEdit
    suggested: (BioEdit, RRID:SCR_007361)
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    MEGA)
    suggested: None
    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.02.04.934232v2 on bioRxiv
  3. Version published to 10.1101/2020.02.04.934232v1 on bioRxiv
  4. Version published to 10.1155/2020/2683286