The folding capacity of the mature domain of the dual-targeted plant tRNA nucleotidyltransferase influences organelle selection
Bibliographic record
Abstract
tRNA-NTs (tRNA nucleotidyltransferases) are required for the maturation or repair of tRNAs by ensuring that they have an intact cytidine-cytidine-adenosine sequence at their 3'-termini. Therefore this enzymatic activity is found in all cellular compartments, namely the nucleus, cytoplasm, plastids and mitochondria, in which tRNA synthesis or translation occurs. A single gene codes for tRNA-NT in plants, suggesting a complex targeting mechanism. Consistent with this, distinct signals have been proposed for plastidic, mitochondrial and nuclear targeting. Our previous research has shown that in addition to N-terminal targeting information, the mature domain of the protein itself modifies targeting to mitochondria and plastids. This suggests the existence of an as yet unknown determinate for the distribution of dual-targeted proteins between these two organelles. In the present study, we explore the enzymatic and physicochemical properties of tRNA-NT variants to correlate the properties of the enzyme with the intracellular distribution of the protein. We show that alteration of tRNA-NT stability influences its intracellular distribution due to variations in organelle import capacities. Hence the fate of the protein is determined not only by the transit peptide sequence, but also by the physicochemical properties of the mature protein.
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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 | 0.000 |
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".