Bibliographic record
Abstract
Post-translational modifications of cellular substrates with ubiquitin and ubiquitin-like proteins (UBLs), including ubiquitin, SUMOs, and neural precursor cell-expressed developmentally downregulated protein 8, play a central role in regulating many aspects of cell biology. The UBL conjugation cascade is initiated by a family of ATP-dependent enzymes termed E1 activating enzymes and executed by the downstream E2-conjugating enzymes and E3 ligases. Despite their druggability and their key position at the apex of the cascade, pharmacologic modulation of E1s with potent and selective drugs has remained elusive until 2009. Among the eight E1 enzymes identified so far, those initiating ubiquitylation (UBA1), SUMOylation (SAE), and neddylation (NAE) are the most characterized and are implicated in various aspects of cancer biology. To date, over 40 inhibitors have been reported to target UBA1, SAE, and NAE, including the NAE inhibitor pevonedistat, evaluated in more than 30 clinical trials. In this Review, we discuss E1 enzymes, the rationale for their therapeutic targeting in cancer, and their different inhibitors, with emphasis on the pharmacologic properties of adenosine sulfamates and their unique mechanism of action, termed substrate-assisted inhibition. Moreover, we highlight other less-characterized E1s-UBA6, UBA7, UBA4, UBA5, and autophagy-related protein 7-and the opportunities for targeting these enzymes in cancer. SIGNIFICANCE STATEMENT: The clinical successes of proteasome inhibitors in cancer therapy and the emerging resistance to these agents have prompted the exploration of other signaling nodes in the ubiquitin-proteasome system including E1 enzymes. Therefore, it is crucial to understand the biology of different E1 enzymes, their roles in cancer, and how to translate this knowledge into novel therapeutic strategies with potential implications in cancer treatment.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".