BioID-based Identification of Skp Cullin F-box (SCF)β-TrCP1/2 E3 Ligase Substrates*
Bibliographic record
Abstract
The identification of ubiquitin E3 ligase substrates has been challenging, due in part to low-affinity, transient interactions, the rapid degradation of targets and the inability to identify proteins from poorly soluble cellular compartments. SCF(β-TrCP1) and SCF(β-TrCP2) are well-studied ubiquitin E3 ligases that target substrates for proteasomal degradation, and play important roles in Wnt, Hippo, and NFκB signaling. Combining 26S proteasome inhibitor (MG132) treatment with proximity-dependent biotin labeling (BioID) and semiquantitative mass spectrometry, here we identify SCF(β-TrCP1/2) interacting partners. Based on their enrichment in the presence of MG132, our data identify over 50 new putative SCF(β-TrCP1/2) substrates. We validate 12 of these new substrates and reveal previously unsuspected roles for β-TrCP in the maintenance of nuclear membrane integrity, processing (P)-body turnover and translational control. Together, our data suggest that β-TrCP is an important hub in the cellular stress response. The technique presented here represents a complementary approach to more standard IP-MS methods and should be broadly applicable for the identification of substrates for many ubiquitin E3 ligases.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".