Multi-epitope Based Peptide Vaccine Design Using Three Structural Proteins (S, E, and M) of SARS-CoV-2: An In Silico Approach

  1. Arpita Singha Roy
  2. Mahafujul Islam Quadery Tonmoy
  3. Atqiya Fariha
  4. Ithmam Hami
  5. Ibrahim Khalil Afif
  6. Md. Adnan Munim
  7. Mohammad Rahanur Alam
  8. Md. Shahadat Hossain

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Abstract

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the novel coronavirus responsible for the ongoing pandemic of coronavirus disease (COVID-19). No sustainable treatment option is available so far to tackle such a public health threat. Therefore, designing a suitable vaccine to overcome this hurdle asks for immediate attention. In this study, we targeted for a design of multi-epitope based vaccine using immunoinformatics tools. We considered the structural proteins S, E and, M of SARS-CoV-2, since they facilitate the infection of the virus into host cell and using different bioinformatics tools and servers, we predicted multiple B-cell and T-cell epitopes having potential for the required vaccine design. Phylogenetic analysis provided insight on ancestral molecular changes and molecular evolutionary relationship of S, E, and M proteins. Based on the antigenicity and surface accessibility of these proteins, eight epitopes were selected by various B cell and T cell epitope prediction tools. Molecular docking was executed to interpret the binding interactions of these epitopes and three potential epitopes WTAGAAAYY, YVYSRVKNL, and GTITVEELK were selected for their noticeable higher binding affinity scores −9.1, −7.4, and −7.0 kcal/mol, respectively. Targeted epitopes had 91.09% population coverage worldwide. In summary, we identified three epitopes having the most significant properties of designing the peptide-based vaccine against SARS-CoV-2.

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    SciScore for 10.1101/2020.06.13.149880: (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
    Retrieval of Protein Sequences: The FASTA format of S, E, and M protein sequences of SARS-CoV-2 from various geographical areas: Australia, China, USA, Finland, India, Sweden, South Korea were retrieved from National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/).
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)
    Then, the BLAST program from NCBI was used to derive the similar sequences against the proteins.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    All the sequences of the proteins retrieved from the NCBI BLASTP result were aligned separately through the ClustalW algorithm by utilizing the MEGA (version 10.0.5) [21].
    BLASTP
    suggested: (BLASTP, RRID:SCR_001010)
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    MEGA
    suggested: (Mega BLAST, RRID:SCR_011920)
    The aligned sequences were then visualized with the Jalview (version 2.11.0) [22]for the observation of consensus and conserved sequences..
    Jalview
    suggested: (Jalview, RRID:SCR_006459)
    The membrane topology of these proteins was analyzed using TMHMM v2.0 server[23]and was later cross-referenced with the InterPro server[24, 25].
    InterPro
    suggested: (InterPro, RRID:SCR_006695)
    VaxiJen v2.0 server[26]calculates antigenicity depending on physicochemical properties of proteins with the threshold value 0.4 (for viral protein sequence).
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    Based on artificial neural network, ABCpred v2.0 server[27–30] predicted the linear B cell epitopes.
    ABCpred
    suggested: None

    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 18. 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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  2. Version published to 10.1101/2020.06.13.149880v1 on bioRxiv