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.
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 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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".