Substrate recognition by the human mitochondrial processing peptidase and its processing of PINK1
Bibliographic record
Abstract
Abstract Nuclear-encoded mitochondrial proteins rely on N-terminal targeting sequences (N-MTS) for their import. These N-MTSs interact with the translocation machinery and are cleaved in the matrix by the mitochondrial processing peptidase (MPP), a heterodimeric zinc metalloprotease which is essential for the maturation of imported proteins. Import and cleavage of PINK1, a kinase implicated in Parkinson’s disease, govern its ability to sense mitochondrial damage, but the MPP cleavage site and its role in PINK1’s function remains cryptic. MPP typically cleaves a unique motif in N-MTSs with an arginine in the P2 position, but how MPP recognizes this motif is unclear. Here, we show that recombinant human MPP cleaves PINK1 between Ala28 and Tyr29 yet is turned over slowly compared to other canonical N-MTSs. While MPP cleavage is not required for downstream PARL processing or PINK1 accumulation in cells, the PINK1 N-MTS binds potently to MPP and inhibits the cleavage of other N-MTSs by glueing the regulatory (α) and catalytic (β) subunits. Finally, we utilize hydrogen-deuterium exchange mass spectrometry to reveal the binding site of the PINK1 N-MTS on MPP. Taken together, our work provides key insight into both the PINK1 import pathway and the mechanisms of MPP processing.
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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) | 0.001 | 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".