1. Biochemistry and Chemical Biology

High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display

  1. Allyson Li
  2. Rashmi Voleti
  3. Minhee Lee
  4. Dejan Gagoski
  5. Neel H Shah  Is a corresponding author
  1. Columbia University, United States
https://doi.org/10.7554/eLife.82345
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  1. Allyson Li
  2. Rashmi Voleti
  3. Minhee Lee
  4. Dejan Gagoski
  5. Neel H Shah
(2023)
High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
eLife 12:e82345.
https://doi.org/10.7554/eLife.82345
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Abstract

Tyrosine kinases and SH2 (phosphotyrosine recognition) domains have binding specificities that depend on the amino acid sequence surrounding the target (phospho)tyrosine residue. Although the preferred recognition motifs of many kinases and SH2 domains are known, we lack a quantitative description of sequence specificity that could guide predictions about signaling pathways or be used to design sequences for biomedical applications. Here, we present a platform that combines genetically-encoded peptide libraries and deep sequencing to profile sequence recognition by tyrosine kinases and SH2 domains. We screened several tyrosine kinases against a million-peptide random library and used the resulting profiles to design high-activity sequences. We also screened several kinases against a library containing thousands of human proteome-derived peptides and their naturally-occurring variants. These screens recapitulated independently measured phosphorylation rates and revealed hundreds of phosphosite-proximal mutations that impact phosphosite recognition by tyrosine kinases. We extended this platform to the analysis of SH2 domains and showed that screens could predict relative binding affinities. Finally, we expanded our method to assess the impact of non-canonical and post-translationally modified amino acids on sequence recognition. This specificity profiling platform will shed new light on phosphotyrosine signaling and could readily be adapted to other protein modification/recognition domains.

Data availability

All of the processed data from the high-throughput specificity screens are provided as source data files. The raw fastq and fasta sequencing files are available as a Dryad repository (DOI: 10.5061/dryad.0zpc86727). Custom code used to process/analyze screening data can be found in a GitHub repository, as specified in the manuscript.

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

Article and author information

Author details

  1. Allyson Li

    Department of Chemistry, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2359-7703

  2. Rashmi Voleti

    Department of Chemistry, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Minhee Lee

    Department of Chemistry, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Dejan Gagoski

    Department of Chemistry, Columbia University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Neel H Shah

    Department of Chemistry, Columbia University, New York, United States
    For correspondence
    neel.shah@columbia.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1186-0626

Funding

National Institute of General Medical Sciences (R35GM138014)

  • Neel H Shah

Damon Runyon Cancer Research Foundation (DFS 31-18)

  • Neel H Shah

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

Reviewing Editor

  1. Tony Hunter, Salk Institute for Biological Studies, United States

Publication history

  1. Received: August 1, 2022
  2. Accepted: March 15, 2023
  3. Accepted Manuscript published: March 16, 2023 (version 1)

Copyright

© 2023, Li 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. Allyson Li
  2. Rashmi Voleti
  3. Minhee Lee
  4. Dejan Gagoski
  5. Neel H Shah
(2023)
High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
eLife 12:e82345.
https://doi.org/10.7554/eLife.82345

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