Recognition of nonproline N-terminal residues by the Pro/N-degron pathway
Bibliographic record
Abstract
Eukaryotic N-degron pathways are proteolytic systems whose unifying feature is their ability to recognize proteins containing N-terminal (Nt) degradation signals called N-degrons, and to target these proteins for degradation by the 26S proteasome or autophagy. GID4, a subunit of the GID ubiquitin ligase, is the main recognition component of the proline (Pro)/N-degron pathway. GID4 targets proteins through their Nt-Pro residue or a Pro at position 2, in the presence of specific downstream sequence motifs. Here we show that human GID4 can also recognize hydrophobic Nt-residues other than Pro. One example is the sequence Nt-IGLW, bearing Nt-Ile. Nt-IGLW binds to wild-type human GID4 with a K d of 16 μM, whereas the otherwise identical Nt-Pro–bearing sequence PGLW binds to GID4 more tightly, with a K d of 1.9 μM. Despite this difference in affinities of GID4 for Nt-IGLW vs. Nt-PGLW, we found that the GID4-mediated Pro/N-degron pathway of the yeast Saccharomyces cerevisiae can target an Nt-IGLW–bearing protein for rapid degradation. We solved crystal structures of human GID4 bound to a peptide bearing Nt-Ile or Nt-Val. We also altered specific residues of human GID4 and measured the affinities of resulting mutant GID4s for Nt-IGLW and Nt-PGLW, thereby determining relative contributions of specific GID4 residues to the GID4-mediated recognition of Nt-Pro vs. Nt-residues other than Pro. These and related results advance the understanding of targeting by the Pro/N-degron pathway and greatly expand the substrate recognition range of the GID ubiquitin ligase in both human and yeast cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".