1. Biochemistry and Chemical Biology
  2. Structural Biology and Molecular Biophysics
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Uncovering the basis of protein-protein interaction specificity with a combinatorially complete library

  1. Thuy-Lan V Lite
  2. Robert A Grant
  3. Isabel Nocedal
  4. Megan L Littlehale
  5. Monica S Guo
  6. Michael T Laub  Is a corresponding author
  1. Massachusetts Institute of Technology, United States
Research Article
  • Cited 3
  • Views 2,360
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Cite this article as: eLife 2020;9:e60924 doi: 10.7554/eLife.60924

Abstract

Protein-protein interaction specificity is often encoded at the primary sequence level. However, the contributions of individual residues to specificity are usually poorly understood and often obscured by mutational robustness, sequence degeneracy, and epistasis. Using bacterial toxin-antitoxin systems as a model, we screened a combinatorially complete library of antitoxin variants at three key positions against two toxins. This library enabled us to measure the effect of individual substitutions on specificity in hundreds of genetic backgrounds. These distributions allow inferences about the general nature of interface residues in promoting specificity. We find that positive and negative contributions to specificity are neither inherently coupled nor mutually exclusive. Further, a wild-type antitoxin appears optimized for specificity as no substitutions improve discrimination between cognate and non-cognate partners. By comparing crystal structures of paralogous complexes, we provide a rationale for our observations. Collectively, this work provides a generalizable approach to understanding the logic of molecular recognition.

Data availability

Diffraction data have been deposited in PDB under the accession code 6X0A. Datasets generated during this study have been deposited in GEO. Raw data, variant frequency, and variant fitness scores can be found under the accession number GSE153897.

The following data sets were generated

Article and author information

Author details

  1. Thuy-Lan V Lite

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  2. Robert A Grant

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  3. Isabel Nocedal

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4706-1113
  4. Megan L Littlehale

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  5. Monica S Guo

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    No competing interests declared.
  6. Michael T Laub

    Department of Biology, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    laub@mit.edu
    Competing interests
    Michael T Laub, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8288-7607

Funding

National Institutes of Health (T32GM007753)

  • Thuy-Lan V Lite

Howard Hughes Medical Institute

  • Michael T Laub

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

Reviewing Editor

  1. Christian R Landry, Université Laval, Canada

Publication history

  1. Received: July 10, 2020
  2. Accepted: October 26, 2020
  3. Accepted Manuscript published: October 27, 2020 (version 1)
  4. Version of Record published: November 16, 2020 (version 2)

Copyright

© 2020, Lite 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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