Landscape and Selection of Vaccine Epitopes in SARS-CoV-2

  1. Christof C. Smith
  2. Sarah Entwistle
  3. Caryn Willis
  4. Steven Vensko
  5. Wolfgang Beck
  6. Jason Garness
  7. Maria Sambade
  8. Eric Routh
  9. Kelly Olsen
  10. Julia Kodysh
  11. Timothy O’Donnell
  12. Carsten Haber
  13. Kirsten Heiss
  14. Volker Stadler
  15. Erik Garrison
  16. Oliver C. Grant
  17. Robert J. Woods
  18. Mark Heise
  19. Benjamin G. Vincent
  20. Alex Rubinsteyn

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Abstract

There is an urgent need for a vaccine with efficacy against SARS-CoV-2. We hypothesize that peptide vaccines containing epitope regions optimized for concurrent B cell, CD4 + T cell, and CD8 + T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE). Additionally, such vaccines can be rapidly manufactured in a distributed manner. In this study, we combine computational prediction of T cell epitopes, recently published B cell epitope mapping studies, and epitope accessibility to select candidate peptide vaccines for SARS-CoV-2. We begin with an exploration of the space of possible T cell epitopes in SARS-CoV-2 with interrogation of predicted HLA-I and HLA-II ligands, overlap between predicted ligands, protein source, as well as concurrent human/murine coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, viral source protein abundance, sequence conservation, coverage of high frequency HLA alleles and co-localization of CD4 + and CD8 + T cell epitopes. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering to select regions with surface accessibility, high sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we propose a set of SARS-CoV-2 vaccine peptides for use in subsequent murine studies and clinical trials.

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    SciScore for 10.1101/2020.06.04.135004: (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

    Antibodies
    SentencesResources
    Antibody epitope curation: Linear B cell epitopes on the SARS-CoV-2 surface glycoprotein were curated from four published studies56–59.
    SARS-CoV-2 surface glycoprotein
    suggested: None
    One study characterized the epitopes of monoclonal neutralizing antibodies57.
    antibodies57
    suggested: None
    Software and Algorithms
    SentencesResources
    HLA ligand prediction: The SARS-CoV-2 protein sequence FASTA was retrieved from the NCBI reference database
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    The proportion of ACE2+ cells expressing the immunomodulatory genes were plotted with the circlize package95.
    circlize
    suggested: (circlize, RRID:SCR_002141)
    Figures 4C, 4D, and 5 were generated using the following Python packages: NumPy118, pandas119, Matplotlib120, and Jupyter121.
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy118
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    One limitation of our study is that, while we use epitope mapping data with direct biological evidence for B cell epitopes in SARS-CoV-2, the T cell epitopes we report were all derived from computational prediction. In an effort to partially overcome this weakness, we applied binding affinity and immunogenicity prediction filters grounded in validated IEDB binding and tetramer studies. Reassuringly, the two extant studies examining T cell responses in COVID-19 patients have identified recurrent T cell epitopes which overlap with the vaccine peptides presented here. Le Bert et al. looked for T cell epitopes within the nucleocapsid (N), NSP-7 and NSP-13 proteins in PBMCs of recovered COVID-19 patients using an IFN-γ ELISpot assay87. They identified two recurrent epitope regions (N101-120, N321-340) which overlap with multiple 27mer vaccine peptides in this paper (Figure 5B, peptides 4-8). Shomuradova et al. also identified COVID-19 patient T cell epitopes, but using A*02:01 tetramers loaded with 13 distinct peptides from the surface glycoprotein (S)88. Two of these 13 peptides showed recurrent reactivity across 14 A*02:01 positive patients (S269-277 and S1000-1008). Both of these epitopes are also included in multiple 27mer vaccine peptides (Figure 5B, peptides 11 and 15). Another potential limitation of this study is the insensitivity of our experiments to the total potential space of SARS-CoV-2 antibody epitopes. Our B cell epitope analyses start with only 58 identified linea...

    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.

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  2. Version published to 10.1101/2020.06.04.135004v1 on bioRxiv