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  1. Author Response

    Reviewer #2 (Public Review):

    Huisman et al. report a method for surveying tens of thousands of peptides for MHC II binding using a yeast display-based approach. The method is shown to cover the SARS-CoV-2 and dengue proteomes, providing a wide-ranging picture of peptides that may be recognized by T cells in infection and that may be used to develop T cell-directed vaccines. Three MHC II alleles are tested, serving as a proof-of-concept for wider application to additional alleles for broadened coverage of human MHC diversity. In addition, the method is directly compared to a computational MHC ligand predictor.

    The study has several strengths. Rigor is strong as the authors survey every 15-mer sequence in an antigen for binding MHC II using overlapping peptide libraries and consider various aspects of the peptide:MHC interaction in their yeast display-based system in defining what is a positive binder. In addition, there are important findings that emerge from the high-throughput MHC II binding approach, such as allele-specific binding preferences at defined positions in the MHC II binding groove, differences in binding motifs between randomized and defined peptide libraries that have implications for training prediction algorithms, and differences between experimental and computational methods for MHC II ligand discovery.

    We thank the reviewer for their comments about our manuscript.

    A discussion about the significance of the observation that the yeast display-based approach identifies MHC II ligands that are not found by NetMHCIIpan4.0 would enhance the paper. This is an important finding, on the one hand, because the method may provide new training data that will improve computational prediction accuracy. On the other hand, many of these sequences are low-affinity binders and may not be immunoreactive as peptide affinity drives T cell response (e.g., PMID: 16039577, PMID: 31253788). How this fits in the context of the oft-heard criticism that computational approaches overpredict would benefit the discussion, as well.

    We have expanded our Discussion to highlight these important discussion points. Specifically, we highlight caveats around low affinity MHC-binding peptides, including effects on immunodominance, as well as examples where low affinity peptides have proven relevant. We also add to our discussion of the utility of these data, emphasizing the potential use of yeast display datasets for augmenting current training data and importance of identifying algorithmic false positives.

    Related to this observation, the authors imply in parts (Abstract, Introduction) that the yeast display method is superior to computational predictions because it identifies MHC II ligands not discovered by computational algorithms, however, the current study is limited to three MHC II alleles, examines only one predictor, and does not provide evidence of T cell validation nor even discussion of the SARS-CoV-2 and dengue datasets in the context of published predictions, MHC II binding data, and immunological studies. The balanced approach taken in the Discussion where experimental and computational approaches are said to complement each other is constructive as it recognizes that both methods have advantages and disadvantages and is a good model for portraying their relationship in earlier parts of the paper.

    We thank the reviewers for this important feedback, and have tempered our language in the Abstract and Introduction to be more balanced, emphasizing how experimental and computational approaches complement one another.

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  2. Evaluation Summary:

    The manuscript builds on previous work to design yeast display libraries representing full viral proteomes with overlapping 15-mer peptides binding to specific HLA-DR alleles, and therefore potentially immunogenic for CD4 T cell responses. The authors use SARS-CoV-2 and dengue viruses as proof of concept and identify a number of potentially immunogenic peptides not predicted by current algorithms. The methods are interesting and promising and will be of interest to a wide range of researchers in immunological and infectious disease studies.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #2 agreed to share their name with the authors.)

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  3. Reviewer #1 (Public Review):

    In this manuscript by Huisman et al., the authors leverage their strong capacity for the development of MHC yeast display to develop a method for high throughput MHC class II binding assessment. They applied their approach to comprehensively screen for HLA-DR401, -402, and -404 binding peptides derived from the whole proteomes of SARS-CoV-2 and four different dengue virus serotypes. The results obtained using this method are carefully analyzed and validated. Minor caveats that come from linker sequences are appropriately described and the context for the utility of this approach is nicely discussed. That the full set of results from these screens is provided makes this paper resourceful to the community.

    Comments:

    The authors should reference and discuss technical differences from the approach previously published by Wen et al. J. Immunological Methods, 2008 which also uses yeast display to identify MHC class II binding peptides derived from the influenza virus genome.

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  4. Reviewer #2 (Public Review):

    Huisman et al. report a method for surveying tens of thousands of peptides for MHC II binding using a yeast display-based approach. The method is shown to cover the SARS-CoV-2 and dengue proteomes, providing a wide-ranging picture of peptides that may be recognized by T cells in infection and that may be used to develop T cell-directed vaccines. Three MHC II alleles are tested, serving as a proof-of-concept for wider application to additional alleles for broadened coverage of human MHC diversity. In addition, the method is directly compared to a computational MHC ligand predictor.

    The study has several strengths. Rigor is strong as the authors survey every 15-mer sequence in an antigen for binding MHC II using overlapping peptide libraries and consider various aspects of the peptide:MHC interaction in their yeast display-based system in defining what is a positive binder. In addition, there are important findings that emerge from the high-throughput MHC II binding approach, such as allele-specific binding preferences at defined positions in the MHC II binding groove, differences in binding motifs between randomized and defined peptide libraries that have implications for training prediction algorithms, and differences between experimental and computational methods for MHC II ligand discovery.

    A discussion about the significance of the observation that the yeast display-based approach identifies MHC II ligands that are not found by NetMHCIIpan4.0 would enhance the paper. This is an important finding, on the one hand, because the method may provide new training data that will improve computational prediction accuracy. On the other hand, many of these sequences are low-affinity binders and may not be immunoreactive as peptide affinity drives T cell response (e.g., PMID: 16039577, PMID: 31253788). How this fits in the context of the oft-heard criticism that computational approaches overpredict would benefit the discussion, as well.

    Related to this observation, the authors imply in parts (Abstract, Introduction) that the yeast display method is superior to computational predictions because it identifies MHC II ligands not discovered by computational algorithms, however, the current study is limited to three MHC II alleles, examines only one predictor, and does not provide evidence of T cell validation nor even discussion of the SARS-CoV-2 and dengue datasets in the context of published predictions, MHC II binding data, and immunological studies. The balanced approach taken in the Discussion where experimental and computational approaches are said to complement each other is constructive as it recognizes that both methods have advantages and disadvantages and is a good model for portraying their relationship in earlier parts of the paper.

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  5. SciScore for 10.1101/2022.02.22.480950: (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
    A positive selection follows, comprised of incubation with anti-Myc-AlexaFluor647 antibody (1:100 volume:volume) and anti-AlexaFluor647 magnetic beads (1:10 volume:volume) and flowed over a Milltenyi column on a magnet at 4 °C, such that yeast with bound peptide are retained on the column.
    anti-Myc-AlexaFluor647
    suggested: None
    anti-AlexaFluor647
    suggested: None
    Recombinant DNA
    SentencesResources
    This extended product was assembled in yeast with linearized pYal vector at a 5:1 insert:vector via electroporation with electrocompetent RJY100 yeast.
    pYal
    suggested: None
    Yeast were then washed into 4 °C acid saline (150mM NaCl, 20mM citric acid, pH5) with 1 μM HLA-DM and incubated at 4 °C overnight.
    pH5
    suggested: None
    Ectodomain sequences of each chain were formatted with a C-terminal poly-histidine purification tag and cloned into pAcGP67a vectors.
    pAcGP67a
    suggested: RRID:Addgene_41812)
    Software and Algorithms
    SentencesResources
    To examine conservation between viruses, viral proteins are aligned using ClustalOmega (Madeira et al., 2019).
    ClustalOmega
    suggested: None
    Amplicons were sequenced on an Illumina MiSeq using paired-end MiSeq v2 300bp kits at the MIT BioMicroCenter.
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    Paired-end reads were assembled using PandaSeq (Masella et al., 2012).
    PandaSeq
    suggested: (PANDAseq, RRID:SCR_002705)
    Each vector was individually transfected into SF9 insect cells (Thermo Fisher) with BestBac 2.0 linearized baculovirus DNA (Expression Systems; Davis, CA) and Cellfectin II Reagent (Thermo Fisher), and propagated to high titer.
    BestBac
    suggested: None
    Relative binding curves were then generated and fit in Prism 9.3 to the equation y = 1/(1+[pep]/IC50), where [pep] is the concentration of un-labelled competitor peptide, in order to determine the concentration of half-maximal inhibition, the IC50 value.
    Prism
    suggested: (PRISM, RRID:SCR_005375)

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

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