Emerging branches of the N-end rule pathways are revealing the sequence complexities of N-termini dependent protein degradation
Bibliographic record
Abstract
The N-end rule links the identity of the N-terminal amino acid of a protein to its in vivo half-life, as some N-terminal residues confer metabolic instability to a protein via their recognition by the cellular machinery that targets them for degradation. Since its discovery, the N-end rule has generally been defined as set of rules of whether an N-terminal residue is stabilizing or not. However, recent studies are revealing that the N-terminal code of amino acids conferring protein instability is more complex than previously appreciated, as recent investigations are revealing that the identity of adjoining downstream residues can also influence the metabolic stability of N-end rule substrate. This is exemplified by the recent discovery of a new branch of N-end rule pathways that target proteins bearing N-terminal proline. In addition, recent investigations are demonstrating that the molecular machinery in N-termini dependent protein degradation may also target proteins for lysosomal degradation, in addition to proteasome-dependent degradation. Herein, we describe some of the recent advances in N-end rule pathways and discuss some of the implications regarding the emerging additional sequence requirements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".