1. Immunology and Inflammation

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding

  1. Brooke D Huisman
  2. Zheng Dai
  3. David K Gifford
  4. Michael E Birnbaum  Is a corresponding author
  1. Massachusetts Institute of Technology, United States
https://doi.org/10.7554/eLife.78589
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  1. Brooke D Huisman
  2. Zheng Dai
  3. David K Gifford
  4. Michael E Birnbaum
(2022)
A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding
eLife 11:e78589.
https://doi.org/10.7554/eLife.78589
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Abstract

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by Major Histocompatibility Complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Data availability

All deep sequencing data are deposited on the Sequence Read Archive (SRA), with accession codes PRJNA806475 [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA806475] and PRJNA708266 [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA708266]. Processed peptide data is provided as a supplemental dataset with this submission. Source data for FP measurements listed in Figure 4 is provided with this submission.

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Brooke D Huisman

    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6229-6498

  2. Zheng Dai

    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.

  3. David K Gifford

    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    David K Gifford, is a founder of ThinkTx..

  4. Michael E Birnbaum

    Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    mbirnb@mit.edu
    Competing interests
    Michael E Birnbaum, is an equity holder in 3T Biosciences, and is a co-founder, equity holder, and consultant of Viralogic Therapeutics and Abata Therapeutics..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2281-3518

Funding

National Institutes of Health (P30-CA14051)

  • Michael E Birnbaum

David and Lucile Packard Foundation

  • Michael E Birnbaum

Schmidt Futures

  • David K Gifford
  • Michael E Birnbaum

National Science Foundation

  • Brooke D Huisman

National Institutes of Health (U19-AI110495)

  • Michael E Birnbaum

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Huisman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Brooke D Huisman
  2. Zheng Dai
  3. David K Gifford
  4. Michael E Birnbaum
(2022)
A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding
eLife 11:e78589.
https://doi.org/10.7554/eLife.78589

Share this article

https://doi.org/10.7554/eLife.78589

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