The de novo synthesis of ubiquitin: identification of deubiquitinases acting on ubiquitin precursors
Bibliographic record
Abstract
Protein ubiquitination, a major post-translational modification in eukaryotes, requires an adequate pool of free ubiquitin. Cells maintain this pool by two pathways, both involving deubiquitinases (DUBs): recycling of ubiquitin from ubiquitin conjugates and processing of ubiquitin precursors synthesized de novo. Although many advances have been made in recent years regarding ubiquitin recycling, our knowledge on ubiquitin precursor processing is still limited, and questions such as when are these precursors processed and which DUBs are involved remain largely unanswered. Here we provide data suggesting that two of the four mammalian ubiquitin precursors, UBA52 and UBA80, are processed mostly post-translationally whereas the other two, UBB and UBC, probably undergo a combination of co- and post-translational processing. Using an unbiased biochemical approach we found that UCHL3, USP9X, USP7, USP5 and Otulin/Gumby/FAM105b are by far the most active DUBs acting on these precursors. The identification of these DUBs together with their properties suggests that each ubiquitin precursor can be processed in at least two different manners, explaining the robustness of the ubiquitin de novo synthesis pathway.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".