Distinct phosphorylation signals drive acceptor versus self-ubiquitination selection by Parkin
Bibliographic record
Abstract
Abstract The RBR E3 ligase parkin is recruited to the outer mitochondrial membrane (OMM) during oxidative stress where it becomes activated and ubiquitinates numerous proteins. Parkin activation involves binding of a phosphorylated ubiquitin (pUb), followed by phosphorylation of parkin itself, both mediated by the OMM kinase, PINK1. However, targeted mitochondrial proteins have little structural or sequence similarity, with the commonality between substrates being proximity to the OMM. Here, we demonstrate that parkin efficiently ubiquitinates a mitochondrial acceptor pre-ligated to pUb and phosphorylation of parkin triggers autoubiquitination activity. Mitochondrial target proteins, Miro1 or CISD1, tethered to pUb are ubiquitinated by parkin more efficiently than if alone or Ub-tethered and ubiquitin molecules are ligated to acceptor protein lysines and not pUb. Parkin phosphorylation is not required for acceptor-pUb ubiquitination. In fact, only phospho-parkin induced self-ubiquitination and deletion of Ubl or mutation at K211N inhibited self-ubiquitination. We propose divergent parkin mechanisms whereby parkin-mediated ubiquitination of acceptor proteins is driven by binding to pre-existing pUb and subsequent parkin phosphorylation triggers autoubiquitination. This finding is critical for understanding parkin’s role in mitochondrial homeostasis and has implications on targets for therapeutics.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".