SARS-CoV-2 Omicron (BA.1 and BA.2) specific novel CD8+ and CD4+ T cell epitopes targeting spike protein

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Abstract

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  1. SciScore for 10.1101/2022.04.05.487186: (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
    Since scores calculated by the SYFPEITHI server vary, we identified peptides that have been experimentally validated to bind to C57BL/6 and BALB/CJ supertypes.
    C57BL/6
    suggested: None
    Software and Algorithms
    SentencesResources
    Retrieval of SARS-CoV-2 sequence: We retrieved the reference S protein sequence of SARS-CoV-2 Wuhan isolates from the NCBI database using accession number YP_009724390.1.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Antigenicity prediction: VaxiJen v2.0 [29] was used to predict antigenic CD8 and CD4 epitopes.
    VaxiJen
    suggested: (VaxiJen, RRID:SCR_018514)
    Allergenicity prediction: AllerTop v2.0 [30] was used to predict allergenicity for both CD8 and CD4 epitopes.
    AllerTop
    suggested: (AllerTop, RRID:SCR_018496)
    Using ProtParam [32] tool on ExPASy, the chemical and physical properties of the top CD8 and CD4 peptides were assessed.
    ProtParam
    suggested: (ProtParam Tool, RRID:SCR_018087)
    ExPASy
    suggested: None
    Since scores calculated by the SYFPEITHI server vary, we identified peptides that have been experimentally validated to bind to C57BL/6 and BALB/CJ supertypes.
    SYFPEITHI
    suggested: None
    We then spatially docked the predicted epitope in the MHC groove using PyMOL and FlexPepDock [39].
    PyMOL
    suggested: (PyMOL, RRID:SCR_000305)

    Results from OddPub: Thank you for sharing your data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Additionally, the PCOptim tool allowed us to determine the caveats in population coverage and thus identify the populations that may receive less coverage. The PCOptim tool provided us with an optimal set of epitopes that reaches the maximum possible population coverage given our raw dataset. Comparing this optimal dataset to our set of top epitopes revealed some overlap, confirming the success of our selection procedure. A total of three epitopes were found in both the optimal dataset and our top epitope datasets. Out of the 27 most common HLA alleles in the human population, we identified 5 alleles that are unaccounted for in the immunogenic, antigenic, non-allergenic, non-toxic, and stable epitope dataset (HLA-B*07:02, HLA-B*08:01, HLA-A*31:01, HLA-A*33:01, and HLA-B*51:01). The difference in population coverage between that optimal dataset and our set of 8 CD8 Omicron-related epitopes can be attributed to the five aforementioned HLA alleles. The only way to increase population coverage further is to include new epitopes that are predicted to be strong binders to the desired HLA alleles and pass the clinical checkpoint filters. While PCOptim does not account for the clinical checkpoint parameters we address in this study, it is a useful tool in determining several epitopes that are useful in obtaining high population coverage. The population coverage that we obtained from our top epitope dataset is substantially high, so adding new epitopes will make a very small impact on...

    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.

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


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