Epitope-Based Peptide Vaccine Against Severe Acute Respiratory Syndrome-Coronavirus-2 Nucleocapsid Protein: An in silico Approach

  1. Ahmed Rakib
  2. Saad Ahmed Sami
  3. Md. Ashiqul Islam
  4. Shahriar Ahmed
  5. Farhana Binta Faiz
  6. Bibi Humayra Khanam
  7. Mir Muhammad Nasir Uddin
  8. Talha Bin Emran

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Abstract

With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. SARS-CoV-2 is a member of the Coronaviridae family, which is transmitted from animal to human and because of being contagious, further it transmitted human to human. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019) as a global pandemic. But, no specific medications are available for the treatment of COVID-19 so far. As a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication as well as interferes with host immune responses. We have comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we have predicted the most immunogenic epitope for T-cell as well as B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.16.100206: (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
    We implied NCBI BLAST (Basic Local Alignment Search Tool) (21) that screens homologous sequences from its database and selects those sequences that are more similar to our SARS-CoV-2 N protein; we also performed multiple sequence alignment (MSA) using the ClustalW web server with default settings, and a phylogenetic tree was assembled using MEGA6 software and a web logo was also generated for the conserved peptide sequences using WebLogo based on this alignment (21–23).
    NCBI BLAST
    suggested: (NCBI BLAST, RRID:SCR_004870)
    ClustalW
    suggested: (ClustalW, RRID:SCR_017277)
    MEGA6
    suggested: (MEGA Software, RRID:SCR_000667)
    WebLogo
    suggested: (WEBLOGO, RRID:SCR_010236)
    2.3 Protein antigenicity and toxicity prediction: To determine the potent antigenic protein of the SARS-CoV-2 N protein, we used the online server VaxiJen v2.0, with a default threshold value (24).
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    Also, we predicted the 3D structure of the protein using EasyModeller, a graphical user interface (GUI) version of MODELLER, where we designed the three-dimensional structure of the SARS-CoV-2 N protein using template proteins from Protein Data Bank.
    MODELLER
    suggested: (MODELLER, RRID:SCR_008395)
    Allergenicity of the predicted epitope was calculated using AllerTop v2.0(35), which is an alignment-free server, used for in silico based allergenicity prediction of a protein-based on its physiochemical properties. 2.8 HLA and epitope interaction analysis using molecular docking studies: 2.9 B-cell epitope identification: The prediction of B-cell epitopes was performed to find the potential antigen that assures humoral immunity.
    AllerTop
    suggested: (AllerTop, RRID:SCR_018496)

    Results from OddPub: Thank you for sharing your data.

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.05.16.100206v1 on bioRxiv