Regulating the Master Regulator: Controlling Ubiquitination by Thinking Outside the Active Site
Bibliographic record
Abstract
The labeling of proteins with ubiquitin/ubiquitin-like (Ubl) proteins is crucial for several physiological processes and in the onset of various diseases. Recently, targeting ubiquitin protein labeling has shifted toward the use of allosteric mechanisms over classical activity-based approaches. Allosteric enzyme regulation offers the potential for greater selectivity and has demonstrated less susceptibility to acquired resistance often associated with active site inhibitors. Furthermore, the isoform diversity among E1 activating, E2 conjugating, E3 ligase, and deubiquitinating (DUB) enzymes offers an ideal platform for modulating activity via allostery. Herein, we have reviewed allosteric inhibitors of the ubiquitin E1-E2-E3 and DUB enzymatic cascade developed over the past decade with a focus on their mechanisms of action. We have highlighted the advantages as well as the challenges associated with designing allosteric modulators of the ubiquitin labeling machinery, and the future promise in targeting these systems using allosteric approaches.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".