Modeling the dynamic behaviors of the COPI vesicle formation regulators, the small GTPase Arf1 and its activating Sec7 guanine nucleotide exchange factor GBF1 on Golgi membranes
Bibliographic record
Abstract
The components and subprocesses underlying the formation of COPI-coated vesicles at the Golgi are well understood. The coating cascade is initiated after the small GTPase Arf1 is activated by the Sec7 domain-containing guanine nucleotide exchange factor GBF1 (Golgi brefeldin A resistant guanine nucleotide exchange factor 1). This causes a conformational shift within Arf1 that facilitates stable association of Arf1 with the membrane, a process required for subsequent recruitment of the COPI coat. Although we have atomic-level knowledge of Arf1 activation by Sec7 domain-containing GEFs, our understanding of the biophysical processes regulating Arf1 and GBF1 dynamics is limited. We used fluorescence recovery after photobleaching data and kinetic Monte Carlo simulation to assess the behavior of Arf1 and GBF1 during COPI vesicle formation in live cells. Our analyses suggest that Arf1 and GBF1 associate with Golgi membranes independently, with an excess of GBF1 relative to Arf1. Furthermore, the GBF1-mediated Arf1 activation is much faster than GBF1 cycling on/off the membrane, suggesting that GBF1 is regulated by processes other than its interactions Arf1. Interestingly, modeling the behavior of the catalytically inactive GBF1/E794K mutant stabilized on the membrane is inconsistent with the formation of a stable complex between it and an endogenous Arf1 and suggests that GBF1/E794K is stabilized on the membrane independently of complex formation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".