A Proximity MAP of RAB GTPases
Bibliographic record
Abstract
ABSTRACT RAB GTPases are the most abundant family of small GTPases and regulate multiple aspects of membrane trafficking events, from cargo sorting to vesicle budding, transport, docking, and fusion. To regulate these processes, RABs are tightly regulated by guanine exchange factors (GEFs) and GTPase-activating proteins (GAPs). Activated RABs recruit effector proteins that regulate trafficking. Identifying RAB-associated proteins has proven to be difficult because their association with interacting proteins is often transient. Recent advances in proximity labeling approaches that allow for the covalent labeling of neighbors of proteins of interest now permit the cataloging of proteins in the vicinity of RAB GTPases. Here, we report APEX2 proximity labeling of 23 human RABs and their neighboring proteomes. We have used bioinformatic analyses to map specific proximal proteins for an extensive array of RAB GTPases, and RAB localization can be inferred from their adjacent proteins. Focusing on specific examples, we identified a physical interaction between RAB25 and DENND6A, which affects cell migration. We also show functional relationships between RAB14 and the EARP complex, or between RAB14 and SHIP164 and its close ortholog UHRF1BP1. Our dataset provides an extensive resource to the community and helps define novel functional connections between RAB GTPases and their neighboring proteins.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".