Substrate recognition by the human mitochondrial processing peptidase and its processing of PINK1
Bibliographic record
Abstract
Nuclear-encoded mitochondrial proteins rely on N-terminal targeting sequences (N-MTS) for their import. Most N-MTSs are cleaved in the matrix by the mitochondrial processing peptidase (MPP), a heterodimeric metalloprotease composed of (α) and catalytic (β) subunits, essential for the maturation of imported proteins. Import and processing of phosphatase and tensin homolog-induced putative kinase 1 (PINK1), a kinase implicated in Parkinson's disease, govern its ability to sense mitochondrial damage. The current paradigm suggests that PINK1 undergoes two sequential processing steps: first, MPP removes the PINK1 N-MTS in the matrix; second, the inner mitochondrial membrane protease PARL cleaves the PINK1 transmembrane domain, leading to PINK1 degradation. Upon depolarization, PINK1 escapes proteolysis and accumulates on mitochondria to initiate mitophagy. However, the MPP cleavage site on PINK1, the role of MPP in PINK1 signaling, and the mechanisms of substrate recognition by human MPP remain unclear. Here, we define the MPP cleavage site on PINK1 between Ala28 and Tyr29 and show that it is inefficiently processed compared with canonical N-MTSs. In cells, MPP cleavage is dispensable for both PARL processing and PINK1 function, decoupling PINK1 import and damage sensing from its N-MTS removal. However, in vitro, the PINK1 N-MTS binds potently to MPP, inhibits the cleavage of other substrates, and traps MPP in a slowly processing complex. Exploiting PINK1 as a mechanistic probe, we use hydrogen-deuterium exchange mass spectrometry to map the PINK1 binding site on MPPα. We identify a two-step mechanism involving MPPα lid rearrangement followed by active site engagement, providing key insight into PINK1's unique import pathway and fundamental MPP processing mechanisms.
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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".