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Record W2774527675 · doi:10.1002/cpmb.36

Analysis of Membrane Protein Interactions with a Bacterial Adenylate Cyclase–Based Two‐Hybrid (BACTH) Technique

2017· article· en· W2774527675 on OpenAlexfundno aff
Scot P. Ouellette, Gouzel Karimova, Marilyne Davi, Daniel Ladant

Bibliographic record

VenueCurrent Protocols in Molecular Biology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueInstitut national de la recherche scientifique
KeywordsCyclaseAdenylate kinaseOperonlac operonIntegral membrane proteinBiologyMembrane proteinBiochemistryMaltose-binding proteinGeneEscherichia coliChemistryCell biologyFusion proteinMembraneRecombinant DNAReceptor

Abstract

fetched live from OpenAlex

The bacterial two-hybrid (BACTH, for "Bacterial Adenylate Cyclase-based Two-Hybrid") technique is a simple and fast genetic approach to analyze protein-protein interactions in vivo. In this system, the proteins of interest are genetically fused to two complementary fragments from the catalytic domain of Bordetella pertussis adenylate cyclase and co-expressed in strains of Escherichia coli deficient in adenylate cyclase. Association of the hybrid proteins restores synthesis of cyclic AMP (cAMP), which then triggers the expression of catabolic operons such as the lactose operon or the maltose regulon. As BACTH uses a cAMP second messenger, the association between the chimeric proteins can take place at a distance from the transcription machinery. This technique is therefore particularly appropriate for studying interactions involving integral-membrane or membrane-associated proteins that may not be soluble in the cytoplasm, and/or that may only associate in the plane of the membrane. This unit describes the basic procedures to characterize protein-protein interactions with the BACTH genetic system and to search for potential partners of known proteins. © 2017 by John Wiley & Sons, Inc.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.355
Teacher spread0.334 · 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 teacher head, not a consensus.

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

Citations40
Published2017
Admission routes1
Has abstractyes

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