Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads

  1. Huy Quoc Nguyen
  2. Jagoree Roy
  3. Bjorn Harink
  4. Nikhil P Damle
  5. Naomi R Latorraca
  6. Brian C Baxter
  7. Kara Brower
  8. Scott A Longwell
  9. Tanja Kortemme
  10. Kurt S Thorn
  11. Martha S Cyert
  12. Polly Morrell Fordyce  Is a corresponding author
  1. Stanford University, United States
  2. University of California, San Francisco, United States

Abstract

Transient, regulated binding of globular protein domains to Short Linear Motifs (SLiMs) in disordered regions of other proteins drives cellular signaling. Mapping the energy landscapes of these interactions is essential for deciphering and perturbing signaling networks but is challenging due to their weak affinities. We present a powerful technology (MRBLE-pep) that simultaneously quantifies protein binding to a library of peptides directly synthesized on beads containing unique spectral codes. Using MRBLE-pep, we systematically probe binding of human calcineurin (CN), a conserved protein phosphatase essential for the immune response and target of immunosuppressants, to the PxIxIT SLiM. We discover that flanking residues and post-translational modifications critically contribute to PxIxIT-CN affinity and identify CN-binding peptides based on multiple scaffolds with a wide range of affinities. The quantitative biophysical data provided by this approach will improve computational modeling efforts, elucidate a broad range of weak protein-SLiM interactions, and revolutionize our understanding of signaling networks.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. In addition, all data generated or analyzed during this study are available in an associated public OSF repository (DOI 10.17605/OSF.IO/FPVE2).

The following data sets were generated

Article and author information

Author details

  1. Huy Quoc Nguyen

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Jagoree Roy

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Bjorn Harink

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Nikhil P Damle

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Naomi R Latorraca

    Biophysics Program, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Brian C Baxter

    Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Kara Brower

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Scott A Longwell

    Department of Bioengineering, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Tanja Kortemme

    Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Kurt S Thorn

    Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Martha S Cyert

    Department of Biology, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3825-7437
  12. Polly Morrell Fordyce

    Department of Genetics, Stanford University, Stanford, United States
    For correspondence
    pfordyce@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9505-0638

Funding

National Institute of General Medical Sciences (DP2GM123641)

  • Polly Morrell Fordyce

National Institute of General Medical Sciences (R01GM107132)

  • Kurt S Thorn

National Institute of General Medical Sciences (R01GM119336)

  • Martha S Cyert

National Institute of General Medical Sciences (R01GM117189)

  • Tanja Kortemme

National Institute of General Medical Sciences (R01GM110089)

  • Tanja Kortemme

Chan Zuckerberg Biohub

  • Tanja Kortemme

Chan Zuckerberg Biohub

  • Polly Morrell Fordyce

Sloan Foundation

  • Polly Morrell Fordyce

Beckman Foundation

  • Polly Morrell Fordyce

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

Reviewing Editor

  1. Michael A Marletta, University of California, Berkeley, United States

Publication history

  1. Received: August 8, 2018
  2. Accepted: July 3, 2019
  3. Accepted Manuscript published: July 8, 2019 (version 1)
  4. Version of Record published: September 5, 2019 (version 2)

Copyright

© 2019, Nguyen 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. Huy Quoc Nguyen
  2. Jagoree Roy
  3. Bjorn Harink
  4. Nikhil P Damle
  5. Naomi R Latorraca
  6. Brian C Baxter
  7. Kara Brower
  8. Scott A Longwell
  9. Tanja Kortemme
  10. Kurt S Thorn
  11. Martha S Cyert
  12. Polly Morrell Fordyce
(2019)
Quantitative mapping of protein-peptide affinity landscapes using spectrally encoded beads
eLife 8:e40499.
https://doi.org/10.7554/eLife.40499

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