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Organization and Assembly of the TRAPPII Complex

2011· article· en· W2123878231 on OpenAlexafffund
Catherine Choi, Michael Davey, Cayetana Schluter, Preet Pandher, Yuan Fang, Leonard J. Foster, Elizabeth Conibear

Bibliographic record

VenueTraffic · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsChild and Family Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsBiologyGolgi apparatusProtein subunitCell biologyVesicular Transport ProteinsYeastTetheringSaccharomyces cerevisiaeEndoplasmic reticulumBiochemistryGene

Abstract

fetched live from OpenAlex

Current models suggest that TRAPP tethering complexes exist in two forms. Whereas the seven-subunit TRAPPI complex mediates ER-to-Golgi transport, TRAPPII contains three additional subunits (Trs65, Trs120 and Trs130) and is required for distinct tethering events at Golgi membranes. It is not clear how TRAPPII assembly is regulated. Here, we show that Tca17 is a fourth TRAPPII-specific component, and that Trs65 and Tca17 interact with distinct domains of Trs130 and make different contributions to complex assembly. Whereas Tca17 promotes the stable association of TRAPPII-specific subunits with the core complex, Trs65 stabilizes TRAPPII in an oligomeric form. We show that Trs85, which was previously reported to be a subunit of both TRAPPI and TRAPPII, is not associated with the TRAPPII complex in yeast. However, we find that proteins related to Trs85, Trs65 and Tca17 are part of the same TRAPP complex in mammalian cells. These findings have implications for models of TRAPP complex formation and suggest that TRAPP complexes may be organized differently in yeast and mammals.

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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.200
Teacher spread0.180 · 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

Citations72
Published2011
Admission routes2
Has abstractyes

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