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Record W2030896731 · doi:10.1042/bj20041090

The septin Sept5/CDCrel-1 competes with α-SNAP for binding to the SNARE complex

2005· article· en· W2030896731 on OpenAlexafffund
Crestina L. Beites, Kristen A. CAMPBELL, William S. Trimble

Bibliographic record

VenueBiochemical Journal · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsSeptinSyntaxin 3Munc-18SyntaxinSNARE complexCell biologySTX1AExocytosisBiologyCytokinesisLipid bilayer fusionVesicle fusionChemistryVesicleBiochemistrySecretionMembraneSynaptic vesicleCell divisionCell

Abstract

fetched live from OpenAlex

SNARE (soluble N-ethylmaleimide-sensitive fusion protein attachment protein receptor) proteins are supposed to mediate the docking and/or fusion of the vesicle with the plasma membrane. However, it is not clearly understood how this process is regulated. In a search for potential SNARE regulators, we recently identified septin 5 (Sept5) as a novel SNARE interacting protein. Septins were first identified as filamentous proteins required for cytokinesis in yeast. Several septins have now been identified in mammals but little is known about their functions. We have previously shown that Sept5 is predominantly expressed in the brain, where it associates with vesicles and membranes through its interaction with the SNARE domain of syntaxin 1A. Furthermore, Sept5 appears to inhibit exocytosis, possibly by regulating vesicle targeting and/or fusion events. To gain insight into the role of Sept5, we have mapped the Sept5 domains important for syntaxin binding. We also investigated the ability of Sept5 to bind to syntaxin when in various protein complexes. Although Sept5 cannot bind an nSec1-syntaxin complex, it can bind syntaxin in a SNARE complex. This interaction is occluded by the binding of alpha-SNAP, suggesting that Sept5 may regulate the availability of SNARE proteins through its interaction with syntaxin and the 7 S complex.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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.

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

Citations110
Published2005
Admission routes2
Has abstractyes

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