High incidence of ubiquitin‐like domains in human ubiquitin‐specific proteases
Bibliographic record
Abstract
Ubiquitin-specific proteases (USPs) emerge as key regulators of numerous cellular processes and account for the bulk of human deubiquitinating enzymes (DUBs). Their modular structure, mostly annotated by sequence homology, is believed to determine substrate recognition and subcellular localization. Currently, a large proportion of known human USP sequences are not annotated either structurally or functionally, including regions both within and flanking their catalytic cores. To extend the current understanding of human USPs, we applied consensus fold recognition to the unannotated content of the human USP family. The most interesting discovery was the marked presence of reliably predicted ubiquitin-like (UBL) domains in this family of enzymes. The UBL domain thus appears to be the most frequently occurring domain in the human USP family, after the characteristic catalytic domain. The presence of multiple UBL domains per USP protein, as well as of UBL domains embedded in the USP catalytic core, add to the structural complexity currently recognized for many DUBs. Possible functional roles of the newly uncovered UBL domains of human USPs, including proteasome binding, and substrate and protein target specificities, are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".