ARF: the most misunderstood GTPase I ever knew - why study ARF GAPs
Bibliographic record
Abstract
ADP-ribosylation factors (ARFs) are GTP-binding proteins that were discovered in the early 1980s, shortly after heterotrimeric GTP-binding proteins (G proteins) and nearly simultaneously with RAS. G proteins formed the basis for the signaling paradigm that has been broadly applied to GTPases, including both RAS and ARF. In this paradigm, GTP-binding proteins act as switches. When converted from the GDP-bound form to the GTP-bound form, GTPases bind effector proteins to transduce a signal. This paradigm is consistent, at least in part, with RAS function as RAS•GTP activates effectors to drive cellular responses such as proliferation. ARF, on the other hand, functions outside this paradigm, at least in its first discovered physiological role: regulation of membrane traffic. Nevertheless, ARFs are often generalized as "on" and "off" switches controlling signaling pathways. In this study, we (i) briefly describe the history of the discoveries of three families of GTPases to provide an understanding of the genesis of the G-protein signaling model, (ii) enumerate some key differences between ARFs, RAS, and G proteins (which better fit the paradigm of molecular switches), and (iii) describe an alternate model for ARFs, in which their cycling between GTP binding and hydrolysis mediates cellular activities, rather than ARFs acting as mediators in a signaling cascade. Furthermore, we highlight the key role of GTPase-activating proteins (GAPs) as integral to ARF function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".