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

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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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Megan Leander

Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

Competing interests
No competing interests declared.
Zhuang Liu

Department of Physics, Boston University, Boston, United States

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-4695-7142
Qiang Cui

Department of Physics, Boston University, Boston, United States

For correspondence
qiangcui@bu.edu

Competing interests
Qiang Cui, Reviewing editor, eLife.
Srivatsan Raman

Department of Biochemistry, University of Wisconsin-Madison, Madison, United States

For correspondence
sraman4@wisc.edu

Competing interests
No competing interests declared.

"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.

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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.