Design of T cell epitope-based vaccine candidate for SARS-CoV-2 targeting nucleocapsid and spike protein escape variants

  1. Gabriel Jabbour
  2. Samantha Rego
  3. Vincent Nguyenkhoa
  4. Sivanesan Dakshanamurthy

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Abstract

The current COVID-19 pandemic continues to spread and devastate in the absence of effective treatments, warranting global concern and action. Despite progress in vaccine development, the rise of novel, increasingly infectious SARS-CoV-2 variants makes it clear that our response to the virus must continue to evolve along with it. The use of immunoinformatics provides an opportunity to rapidly and efficiently expand the tools at our disposal to combat the current pandemic and prepare for future outbreaks through epitope-based vaccine design. In this study, we validated and compared the currently available epitope prediction tools, and then used the best tools to predict T cell epitopes from SARS-CoV-2 spike and nucleocapsid proteins for use in an epitope-based vaccine. We combined the mouse MHC affinity predictor and clinical predictors such as HLA affinity, immunogenicity, antigenicity, allergenicity, toxicity and stability to select the highest quality CD8 and CD4 T cell epitopes for the common SARS-CoV-2 variants of concern suitable for further preclinical studies. We also identified variant-specific epitopes to more precisely target the Alpha, Beta, Gamma, Delta, Cluster 5 and US variants. We then modeled the 3D structures of our top 4 N and S epitopes to investigate the molecular interaction between peptide-MHC and peptide-MHC-TCR complexes. Following in vitro and in vivo validation, the epitopes identified by this study may be used in an epitope-based vaccine to protect across all current variants, as well as in variant-specific booster shots to target variants of concern. Immunoinformatics tools allowed us to efficiently predict epitopes in silico most likely to prove effective in vivo, providing a more streamlined process for vaccine development in the context of a rapidly evolving pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2021.09.11.459907: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    Mouse HLA affinity prediction: NetH2pan 4.0 [61,62] was used to predict mouse HLA affinity of CD8 epitopes using MHCI alleles of the most commonly used mouse strains to study COVID-19 vaccines: C57BL/6 and BALB/c rodents.
    C57BL/6
    suggested: None
    BALB/c
    suggested: RRID:IMSR_ORNL:BALB/cRl)
    Software and Algorithms
    SentencesResources
    We selected 28 epitopes from previous papers that had been found to be immunogenic in convalescent COVID-19 patients [27, 31-33] and 20 epitopes from ViPR with positive assay and 100% match with the SARS-CoV-2 N protein reference sequence.
    ViPR
    suggested: (vipR, RRID:SCR_010685)
    Allergenicity prediction: AllerTop 2.0 [57,58] was used to predict allergenicity of both CD8 and CD4 epitopes.
    AllerTop
    suggested: (AllerTop, RRID:SCR_018496)
    Instability index prediction: ProtParam [59] was used to predict the instability index [60] to determine whether an epitope was stable or unstable in vivo.
    ProtParam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    The 3D structures of MHC molecules were downloaded from Protein Database Bank on RCSB.org and processed on PyMOL to remove the epitope.
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    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:
    • 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.

    Results from scite Reference Check: We found no unreliable references.

    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.

  2. Version published to 10.1101/2021.09.11.459907 on bioRxiv