Munc18‐1 as a key regulator of neurosecretion
Bibliographic record
Abstract
Munc18-1 plays essential roles in neurosecretion by interacting with syntaxin-1 and controlling the formation of the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNARE) complex. At least three important functions of Munc18-1 have been proposed: (i) molecular chaperone of syntaxin-1 for appropriate localization and expression of syntaxin-1, (ii) priming/stimulation of the SNARE-mediated membrane fusion, and (iii) docking of large dense-core vesicles to the plasma membrane. Similarly, at least two different binding modes have been proposed for the interaction between Munc18-1 and syntaxin-1: (i) binary binding to a 'closed' conformation of syntaxin-1, and (ii) binding to the N-terminal peptide of syntaxin-1, which is thought to enable an interaction with the quaternary SNARE complex and/or further stabilize the binary interaction between Munc18-1 and closed syntaxin-1. Recent structural analyses have identified critical Munc18-1 residues implicated in these different modes of binding. These have recently been tested functionally in rescue experiments using Munc18-1 null neurons, chromaffin cells and Munc18-1/-2 knockdown PC12 cells, allowing remarkable progress to be made in the structural/functional understanding of Munc18-1. In this review, we summarize these recent advances and attempt to propose an updated model of the pleiotropic functions of Munc18-1 in neuroexocytosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".