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Record W4311932875 · doi:10.1002/1873-3468.14553

The <scp>TRAPP</scp> complexes: oligomeric exchange factors that activate the small <scp>GTPases</scp> Rab1 and Rab11

2022· review· en· W4311932875 on OpenAlexfundno aff
Antonio Galindo, Sean Munro

Bibliographic record

VenueFEBS Letters · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsnot available
FundersMedical Research Council Canada
KeywordsRabGTPaseCell biologyGuanine nucleotide exchange factorChemistryCytokinesisGTP'BiochemistryBiologyEnzymeCellCell division

Abstract

fetched live from OpenAlex

The Transport Protein Particle (TRAPP) complexes are highly conserved multisubunit complexes that act as nucleotide exchange factors (GEFs) for Rab GTPases. They act in both protein secretion and autophagy and have also been proposed to have a role in other processes such as cytokinesis and ciliogenesis. There are two TRAPP complexes in metazoans: TRAPPII, which activates Rab11; and TRAPPIII, which activates Rab1. Both complexes share a core of small subunits that form the active site for the exchange of GDP for GTP. In addition, each TRAPP complex has distinct large subunits that determine the specificity of each complex towards its substrate Rab and are essential for activity in vivo. Crystal structures have revealed the organisation of the TRAPP core and the mechanism of Rab1 activation, whilst recent cryo-EM structures have unveiled the arrangement of the specific subunits around the core to form each complex. Combining these findings with functional experiments has allowed the proposal of mechanisms for how the specificity of each complex towards their cognate Rab is determined and for the arrangement of these large complexes on the membrane.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.005

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 head0.049
GPT teacher head0.252
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2022
Admission routes1
Has abstractyes

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