Comparative Analysis of Mitochondrial N-Termini from Mouse, Human, and Yeast
Bibliographic record
Abstract
The majority of mitochondrial proteins are encoded in the nuclear genome, translated in the cytoplasm, and directed to the mitochondria by an N-terminal presequence that is cleaved upon import. Recently, N-proteome catalogs have been generated for mitochondria from yeast and from human U937 cells. Here, we applied the subtiligase method to determine N-termini for 327 proteins in mitochondria isolated from mouse liver and kidney. Comparative analysis between mitochondrial N-termini from mouse, human, and yeast proteins shows that whereas presequences are poorly conserved at the sequence level, other presequence properties are extremely conserved, including a length of ∼20-60 amino acids, a net charge between +3 to +6, and the presence of stabilizing amino acids at the N-terminus of mature proteins that follow the N-end rule from bacteria. As in yeast, ∼80% of mouse presequence cleavage sites match canonical motifs for three mitochondrial peptidases (MPP, Icp55, and Oct1), whereas the remainder do not match any known peptidase motifs. We show that mature mitochondrial proteins often exist with a spectrum of N-termini, consistent with a model of multiple cleavage events by MPP and Icp55. In addition to analysis of canonical targeting presequences, our N-terminal dataset allows the exploration of other cleavage events and provides support for polypeptide cleavage into two distinct enzymes (Hsd17b4), protein cleavages key for signaling (Oma1, Opa1, Htra2, Mavs, and Bcs2l13), and in several cases suggests novel protein isoforms (Scp2, Acadm, Adck3, Hsdl2, Dlst, and Ogdh). We present an integrated catalog of mammalian mitochondrial N-termini that can be used as a community resource to investigate individual proteins, to elucidate mechanisms of mammalian mitochondrial processing, and to allow researchers to engineer tags distally to the presequence cleavage.
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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 | 0.001 | 0.001 |
| 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".