Structural insight into the human mitochondrial tRNA purine N1-methyltransferase and ribonuclease P complexes
Bibliographic record
Abstract
Mitochondrial tRNAs are transcribed as long polycistronic transcripts of precursor tRNAs and undergo posttranscriptional modifications such as endonucleolytic processing and methylation required for their correct structure and function. Among them, 5′-end processing and purine 9 N1-methylation of mitochondrial tRNA are catalyzed by two proteinaceous complexes with overlapping subunit composition. The Mg 2+ -dependent RNase P complex for 5′-end cleavage comprises the methyltransferase domain–containing protein tRNA methyltransferase 10C, mitochondrial RNase P subunit (TRMT10C/MRPP1), short-chain oxidoreductase hydroxysteroid 17β-dehydrogenase 10 (HSD17B10/MRPP2), and metallonuclease KIAA0391/MRPP3. An MRPP1–MRPP2 subcomplex also catalyzes the formation of 1-methyladenosine/1-methylguanosine at position 9 using S -adenosyl-l-methionine as methyl donor. However, a lack of structural information has precluded insights into how these complexes methylate and process mitochondrial tRNA. Here, we used a combination of X-ray crystallography, interaction and activity assays, and small angle X-ray scattering (SAXS) to gain structural insight into the two tRNA modification complexes and their components. The MRPP1 N terminus is involved in tRNA binding and monomer–monomer self-interaction, whereas the C-terminal SPOUT fold contains key residues for S -adenosyl-l-methionine binding and N1-methylation. The entirety of MRPP1 interacts with MRPP2 to form the N1-methylation complex, whereas the MRPP1–MRPP2–MRPP3 RNase P complex only assembles in the presence of precursor tRNA. This study proposes low-resolution models of the MRPP1–MRPP2 and MRPP1–MRPP2–MRPP3 complexes that suggest the overall architecture, stoichiometry, and orientation of subunits and tRNA substrates.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".