Referee report. For: The business of deubiquitination – location, location, location [version 1; referees: 2 approved]
Bibliographic record
Abstract
A majority of proteins in the cell can be modified by ubiquitination, thereby altering their function or stability. This ubiquitination is controlled by both ubiquitinating and deubiquitinating enzymes (DUBs). The number of ubiquitin ligases exceeds that of DUBs by about eightfold, indicating that DUBs may have much broader substrate specificity. Despite this, DUBs have been shown to have quite specific physiological functions. This functional specificity is likely due to very precise regulation of activity arising from the sophisticated use of all mechanisms of enzyme regulation. In this commentary, we briefly review key features of DUBs with more emphasis on regulation. In particular, we focus on localization of the enzymes as a critical regulatory mechanism which when integrated with control of expression, substrate activation, allosteric regulation, and post-translational modifications results in precise spatial and temporal deubiquitination of proteins and therefore specific physiological functions. Identification of compounds that target the structural elements in DUBs that dictate localization may be a more promising approach to development of drugs with specificity of action than targeting the enzymatic activity, which for most DUBs is dependent on a thiol group that can react non-specifically with many compounds in large-scale screening.
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 | 0.008 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.612 | 0.368 |
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".