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Record W2141704011 · doi:10.1073/pnas.0805377105

The role of the C terminus of the SNARE protein SNAP-25 in fusion pore opening and a model for fusion pore mechanics

2008· article· en· W2141704011 on OpenAlexaff
Qinghua Fang, Khajak Berberian, Liang‐Wei Gong, Ismail M. Hafez, Jakob B. Sørensen, Manfred Lindau

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesLundbeckfondenNational Institutes of HealthNational Science Foundation
KeywordsFusionSNARE complexLipid bilayer fusionBiophysicsChemistryAmperometryMutantC-terminusMembraneFusion proteinBiochemistryBiologyRecombinant DNAElectrochemistryAmino acid

Abstract

fetched live from OpenAlex

Formation of a fusion pore between a vesicle and its target membrane is thought to involve the so-called SNARE protein complex. However, there is no mechanistic model explaining how the fusion pore is opened by conformational changes in the SNARE complex. It has been suggested that C-terminal zipping triggers fusion pore opening. A SNAP-25 mutant named SNAP-25Delta9 (lacking the last nine C-terminal residues) should lead to a less-tight C-terminal zipping. Single exocytotic events in chromaffin cells expressing this mutant were characterized by carbon fiber amperometry and cell-attached patch capacitance measurements. Cells expressing SNAP-25Delta9 displayed smaller amperometric "foot-current" currents, reduced fusion pore conductances, and lower fusion pore expansion rates. We propose that SNARE/lipid complexes form proteolipid fusion pores. Fusion pores involving the SNAP-25Delta9 mutant will be less tightly zipped and may lead to a longer fusion pore structure, consistent with the observed decrease of fusion pore conductance.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.251
Teacher spread0.230 · 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

Citations112
Published2008
Admission routes1
Has abstractyes

Explore more

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