Evidence map›Paper›PMID 37662419›Full record

ArticlebioRxiv : the preprint server for biology2024

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery.

Zhuang Liu, Thomas Gillis, Srivatsan Raman, Qiang Cui

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Zhuang LiuDepartment of Physics, Boston University, Boston, United States.ORCID 0000-0003-4695-7142
Thomas GillisDepartment of Biochemistry, University of Wisconsin, Madison, United States.
Srivatsan RamanDepartment of Biochemistry, University of Wisconsin, Madison, United States.ORCID 0000-0003-2461-1589
Qiang CuiDepartment of Physics, Boston University, Boston, United States.ORCID 0000-0001-6214-5211
Boston University · USUniversity of Wisconsin–Madison · US

Funding

Computational Analysis of Enzyme Catalysis and RegulationR35GM141930 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI Qiang Cui · 2021 to 2026
$2.7M
Understanding molecular rules governing protein allostery by deep mutational scanningDP2GM132682 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI RAMAN, SRIVATSAN · 2018 to 2018
$2.1M
NIGMS NIH HHS DP2 GM132682NIGMS NIH HHS R35 GM141930
6 · The paper itself

Abstract

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

Identifiers

PMID37662419
PMCPMC10473640
OpenAlexW4385633445

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.