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Record W2171214873 · doi:10.1074/mcp.t600025-mcp200

A Differential Cytolocalization Assay for Analysis of Macromolecular Assemblies in the Eukaryotic Cytoplasm

2006· article· en· W2171214873 on OpenAlexaff
Daniel Blanchard, Harald Hutter, Jamie Fleenor, Andrew Fire

Bibliographic record

VenueMolecular & Cellular Proteomics · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsSimon Fraser University
FundersNational Institute of General Medical Sciences
KeywordsCaenorhabditis elegansRNA interferenceCytoplasmBiologyCell biologyProtein–protein interactionNonsense-mediated decayRNAGeneBiochemistry

Abstract

fetched live from OpenAlex

We have developed a differential cytolocalization assay (DCLA) that allows the observation of cytoplasmic protein/protein interactions in vivo. In the DCLA, interactions are visualized as a relocalization of a green fluorescent protein-tagged "prey" by a membrane-bound "bait." This assay was tested and utilized in Caenorhabditis elegans to probe interactions among proteins involved in RNA interference (RNAi) and nonsense-mediated decay (NMD) pathways. Several previously documented interactions were confirmed with DCLA, whereas uniformly negative results were obtained in several controls in which no interaction was expected. Novel interactions were also observed, including the association of SMG-5, a protein required for NMD, to several components of the RNAi pathway. The DCLA can be readily carried out under diverse conditions, allowing a dynamic assessment of protein interactions in vivo. We used this property to test a subset of the RNAi and NMD interactions in animals in which proteins central to each mechanism were mutated; several key associations in each machinery that can occur in vivo in the absence of a functional process were identified.

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 score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Citations20
Published2006
Admission routes1
Has abstractyes

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