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Record W3096830135 · doi:10.1002/anie.202008691

Fine‐Tuning Protein Self‐Organization by Orthogonal Chemo‐Optogenetic Tools

2020· article· en· W3096830135 on OpenAlexafffund
Huan Sun, Haiyang Jia, Diego A. Ramirez‐Diaz, Nediljko Budiša, Petra Schwille

Bibliographic record

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Manitoba
FundersChina Scholarship CouncilDeutsche ForschungsgemeinschaftMax-Planck-GesellschaftBundesministerium für Bildung und ForschungCanada Research ChairsCanada Excellence Research Chairs, Government of Canada
KeywordsFtsZTreadmillingGTPaseCell divisionTyrosineCell biologyMutantBiologyChemistryBiophysicsCytoskeletonCellBiochemistry

Abstract

fetched live from OpenAlex

A universal gain-of-function approach for the spatiotemporal control of protein activity is highly desirable when reconstituting biological modules in vitro. Here we used orthogonal translation with a photocaged amino acid to map and elucidate molecular mechanisms in the self-organization of the prokaryotic filamentous cell-division protein (FtsZ) that is highly relevant for the assembly of the division ring in bacteria. We masked a tyrosine residue of FtsZ by site-specific incorporation of a photocaged tyrosine analogue. While the mutant still shows self-assembly into filaments, dynamic self-organization into ring patterns can no longer be observed. UV-mediated uncaging revealed that tyrosine 222 is essential for the regulation of the protein's GTPase activity, self-organization, and treadmilling dynamics. Thus, the light-mediated assembly of functional protein modules appears to be a promising minimal-regulation strategy for building up molecular complexity towards a minimal cell.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.279
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2020
Admission routes2
Has abstractyes

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