Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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

Megan Leander, Zhuang Liu, Qiang Cui, View ORCID ProfileSrivatsan Raman
doi: https://doi.org/10.1101/2022.05.01.490188
Megan Leander
1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI – 53706, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhuang Liu
2Department of Physics, Boston University, Boston, MA – 02215, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qiang Cui
2Department of Physics, Boston University, Boston, MA – 02215, United States
3Department of Chemistry, Boston University, Boston, MA – 02215, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: qiangcui@bu.edu sraman4@wisc.edu
Srivatsan Raman
1Department of Biochemistry, University of Wisconsin-Madison, Madison, WI – 53706, United States
4Department of Bacteriology, University of Wisconsin-Madison, Madison, WI – 53706, United States
5Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI – 53706, United States
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Srivatsan Raman
  • For correspondence: qiangcui@bu.edu sraman4@wisc.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
Back to top
PreviousNext
Posted May 01, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
Megan Leander, Zhuang Liu, Qiang Cui, Srivatsan Raman
bioRxiv 2022.05.01.490188; doi: https://doi.org/10.1101/2022.05.01.490188
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins
Megan Leander, Zhuang Liu, Qiang Cui, Srivatsan Raman
bioRxiv 2022.05.01.490188; doi: https://doi.org/10.1101/2022.05.01.490188

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biochemistry
Subject Areas
All Articles
  • Animal Behavior and Cognition (3689)
  • Biochemistry (7796)
  • Bioengineering (5675)
  • Bioinformatics (21284)
  • Biophysics (10578)
  • Cancer Biology (8174)
  • Cell Biology (11945)
  • Clinical Trials (138)
  • Developmental Biology (6763)
  • Ecology (10401)
  • Epidemiology (2065)
  • Evolutionary Biology (13866)
  • Genetics (9708)
  • Genomics (13073)
  • Immunology (8146)
  • Microbiology (20014)
  • Molecular Biology (7853)
  • Neuroscience (43056)
  • Paleontology (319)
  • Pathology (1279)
  • Pharmacology and Toxicology (2258)
  • Physiology (3351)
  • Plant Biology (7232)
  • Scientific Communication and Education (1312)
  • Synthetic Biology (2006)
  • Systems Biology (5538)
  • Zoology (1128)