Attenuated Subcomponent Vaccine Design Targeting the SARS-CoV-2 Nucleocapsid Phosphoprotein RNA Binding Domain: In Silico Analysis

  1. Onyeka S. Chukwudozie
  2. Rebecca C. Chukwuanukwu
  3. Onyekachi O. Iroanya
  4. Daniel M. Eze
  5. Vincent C. Duru
  6. Temiloluwa O. Dele-Alimi
  7. Busuyi D. Kehinde
  8. Taiwo T. Bankole
  9. Perpetua C. Obi
  10. Elizabeth U. Okinedo

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has previously never been identified with humans, thereby creating devastation in public health. The need for an effective vaccine to curb this pandemic cannot be overemphasized. In view of this, we designed a subcomponent antigenic peptide vaccine targeting the N-terminal (NT) and C-terminal (CT) RNA binding domains of the nucleocapsid protein that aid in viral replication. Promising antigenic B cell and T cell epitopes were predicted using computational pipelines. The peptides “RIRGGDGKMKDL” and “AFGRRGPEQTQGNFG” were the B cell linear epitopes with good antigenic index and nonallergenic property. Two CD8 + and Three CD4 + T cell epitopes were also selected considering their safe immunogenic profiling such as allergenicity, antigen level conservancy, antigenicity, peptide toxicity, and putative restrictions to a number of MHC-I and MHC-II alleles. With these selected epitopes, a nonallergenic chimeric peptide vaccine incapable of inducing a type II hypersensitivity reaction was constructed. The molecular interaction between the Toll-like receptor-5 (TLR5) which was triggered by the vaccine was analyzed by molecular docking and scrutinized using dynamics simulation. Finally, in silico cloning was performed to ensure the expression and translation efficiency of the vaccine, utilizing the pET-28a vector. This research, therefore, provides a guide for experimental investigation and validation.

Article activity feed

  1. Version published to 10.1155/2020/2837670
  2. ScreenIT

    SciScore for 10.1101/2020.06.30.176537: (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 physiochemical properties of the protein sequence were assessed and bio-computed via an online tool Protparam [14]
    Protparam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    The Elliprot server (http://tools.iedb.org/ellipro/) was adopted for this purpose, while Pymol was utilized to examine the positions of forecast epitopes on the 3D structure of SARS-CoV-2 nucleocapsid phosphoprotein [17].
    Pymol
    suggested: (PyMOL, RRID:SCR_000305)
    The antigenic properties of the epitopes were studied using the Vaxijen 2.0 server set at a threshold of 0.6.
    Vaxijen
    suggested: (VaxiJen, RRID:SCR_018514)
    Validation of predicted conjugated Peptide Vaccine 3D model: The confirmation of the selected 3D model predicted by I-TASSER was further validated by the Ramachandran plot.
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)
    RAMPAGE and MolProbity [20] online servers were employed for the estimation of selected 3D model quality.
    MolProbity
    suggested: (MolProbity, RRID:SCR_014226)
    The Ramachandran plot obtained from RAMPAGE describes a good quality model that has over 70% residues in the most favored region.
    RAMPAGE
    suggested: (RAMPAGE, RRID:SCR_017590)
    ProSA specifically faces the needs confronted in the authentication of protein structures acquired from X-ray analysis, NMR spectroscopy, and hypothetical estimations.
    ProSA
    suggested: (ProSA-web, RRID:SCR_018540)
    Protein-protein docking of the peptide vaccine and the human toll-like receptor-5 (TLR5): In this study, molecular docking analysis between vaccine and human toll-like receptor-5 was performed using ClusPro 2.2 protein-protein interaction online server [22].
    ClusPro
    suggested: (ClusPro, RRID:SCR_018248)

    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 26 and 27. 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.
    • No funding statement was detected.
    • 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.

  3. Version published to 10.1101/2020.06.30.176537v1 on bioRxiv