Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins

  1. Megan Leander
  2. Zhuang Liu
  3. Qiang Cui  Is a corresponding author
  4. Srivatsan Raman  Is a corresponding author
  1. University of Wisconsin-Madison, United States
  2. Boston University, United States

Abstract

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTF) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed that global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

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Data included in the manuscript

Article and author information

Author details

  1. Megan Leander

    Department of Biochemistry, University of Wisconsin-Madison, Madison, United States
    Competing interests
    No competing interests declared.
  2. Zhuang Liu

    Department of Physics, Boston University, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4695-7142
  3. Qiang Cui

    Department of Physics, Boston University, Boston, United States
    For correspondence
    qiangcui@bu.edu
    Competing interests
    Qiang Cui, Reviewing editor, eLife.
  4. Srivatsan Raman

    Department of Biochemistry, University of Wisconsin-Madison, Madison, United States
    For correspondence
    sraman4@wisc.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2461-1589

Funding

National Institutes of Health (DP2GM132682)

  • Srivatsan Raman

National Institutes of Health (R35GM141930)

  • Qiang Cui

National Institutes of Health (T32GM08293)

  • Megan Leander

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

Reviewing Editor

  1. José D Faraldo-Gómez, National Institutes of Health, United States

Version history

  1. Preprint posted: May 1, 2022 (view preprint)
  2. Received: May 3, 2022
  3. Accepted: October 13, 2022
  4. Accepted Manuscript published: October 13, 2022 (version 1)
  5. Version of Record published: November 14, 2022 (version 2)

Copyright

© 2022, Leander 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. Megan Leander
  2. Zhuang Liu
  3. Qiang Cui
  4. Srivatsan Raman
(2022)
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
eLife 11:e79932.
https://doi.org/10.7554/eLife.79932

Share this article

https://doi.org/10.7554/eLife.79932

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