Evidence map›Paper›PMID 37562980›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins.

Jan Mathony, Sabine Aschenbrenner, Philipp Becker, Dominik Niopek

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  6. Dissecting the Determinants of Domain Insertion Tolerance and Allostery in Proteins.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jan MathonyCenter for Synthetic Biology, Technical University of Darmstadt, 64287, Darmstadt, Germany.ORCID 0000-0003-4081-9953
Sabine AschenbrennerInstitute of Pharmacy and Molecular Biotechnology (IPMB), Faculty of Engineering Sciences, Heidelberg University, 69120, Heidelberg, Germany.
Philipp BeckerCenter for Synthetic Biology, Technical University of Darmstadt, 64287, Darmstadt, Germany.
Dominik NiopekInstitute of Pharmacy and Molecular Biotechnology (IPMB), Faculty of Engineering Sciences, Heidelberg University, 69120, Heidelberg, Germany.ORCID 0000-0001-7479-530X

Funding

ERC 101041570German Research Foundation 453202693the German Academic Scholarship Foundationthe Schwiete Stiftung, the Aventis foundation
6 · The paper itself

Abstract

Domain insertion engineering is a promising approach to recombine the functions of evolutionarily unrelated proteins. Insertion of light-switchable receptor domains into a selected effector protein, for instance, can yield allosteric effectors with light-dependent activity. However, the parameters that determine domain insertion tolerance and allostery are poorly understood. Here, an unbiased screen is used to systematically assess the domain insertion permissibility of several evolutionary unrelated proteins. Training machine learning models on the resulting data allow to dissect features informative for domain insertion tolerance and revealed sequence conservation statistics as the strongest indicators of suitable insertion sites. Finally, extending the experimental pipeline toward the identification of switchable hybrids results in opto-chemogenetic derivatives of the transcription factor AraC that function as single-protein Boolean logic gates. The study reveals determinants of domain insertion tolerance and yielded multimodally switchable proteins with unique functional properties.

Indexed as

Transcription FactorsAllosteric RegulationTranscription Factorsallosterydomain insertionoptogeneticsprotein engineering

Identifiers

PMID37562980
PMCPMC10558690

What OpenQuestion holds

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Registered trials

None linked

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.