Dissecting the mechanism of Ca<sup>2+</sup>‐triggered membrane fusion: Probing protein function using thiol reactivity
Bibliographic record
Abstract
1. Ca(2+)-triggered membrane fusion involves the coordinated actions of both lipids and proteins, but the specific mechanisms remain poorly understood. The urchin cortical vesicle model is a stage-specific native preparation fully enabling the directly coupled functional-molecular analyses necessary to identify critical components of fast triggered membrane fusion. 2. Recent work on lipidic components has established a direct role for cholesterol in the fusion mechanism via local contribution of negative curvature to readily enable the formation of transient lipidic fusion intermediates. In addition, cholesterol- and sphingomyelin-enriched domains regulate the efficiency of fusion by focally organizing other components to ensure an optimized response to the triggering Ca(2+) transient. 3. There is less known about the identity of proteins involved in the Ca(2+)-triggering steps of membrane fusion. Thiol reagents can be used as unbiased tools to probe protein functions. Comparisons of several thiol-reactive reagents have identified different effects on Ca(2+) sensitivity and the extent of fusion, suggesting that there are at least two distinct thiol sites that participate in the fusion mechanism: one that regulates the efficiency of Ca(2+) sensing/triggering and one that may function during the membrane merger event itself. 4. To identify the proteins that regulate Ca(2+) sensitivity, the fluorescent thiol reagent Lucifer yellow iodoacetamide was used to potentiate fusion and simultaneously tag the proteins involved. Ongoing work involves the isolation of cholesterol-enriched membrane fractions to reduce the complexity of the labelled proteome, narrowing the number of candidate proteins.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".