tRNA elbow modifications affect the tRNA pseudouridine synthase TruB and the methyltransferase TrmA
Bibliographic record
Abstract
tRNAs constitute the most highly modified class of RNA. Every tRNA contains a unique set of modifications, and Ψ55, m 5 U54, and m 7 G46 are frequently found within the elbow of the tRNA structure. Despite the abundance of tRNA modifications, we are only beginning to understand the orchestration of modification enzymes during tRNA maturation. Here, we investigated whether pre-existing modifications impact the binding affinity or catalysis by tRNA elbow modification enzymes. Specifically, we focused on the Escherichia coli enzymes TruB, TrmA, and TrmB which generate Ψ55, m 5 U54, and m 7 G46, respectively. tRNAs containing a single modification were prepared, and the binding and activity preferences of purified E. coli TrmA, TruB, and TrmB were examined in vitro. TruB preferentially binds and modifies unmodified tRNA. TrmA prefers to modify unmodified tRNA, but binds most tightly to tRNA that already contains Ψ55. In contrast, binding and modification by TrmB is insensitive to the tRNA modification status. Our results suggest that TrmA and TruB are likely to act on mostly unmodified tRNA precursors during the early stages of tRNA maturation whereas TrmB presumably acts on later tRNA intermediates that are already partially modified. In conclusion, we uncover the mechanistic basis for the preferred modification order in the E. coli tRNA elbow region.
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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".