LeuRS can leucylate type I and type II tRNALeus in Streptomyces coelicolor
Bibliographic record
Abstract
Transfer RNAs (tRNAs) are divided into two types, type I with a short variable loop and type II with a long variable loop. Aminoacylation of type I or type II tRNALeu is catalyzed by their cognate leucyl-tRNA synthetases (LeuRSs). However, in Streptomyces coelicolor, there are two types of tRNALeu and only one LeuRS (ScoLeuRS). We found that the enzyme could leucylate both types of ScotRNALeu, and had a higher catalytic efficiency for type II ScotRNALeu(UAA) than for type I ScotRNALeu(CAA). The results from tRNA and enzyme mutagenesis showed that ScoLeuRS did not interact with the canonical discriminator A73. The number of nucleotides, rather than the type of base of the variable loop in the two types of ScotRNALeus, was determined as important for aminoacylation. In vitro and in vivo assays showed that the tertiary structure formed by the D-loop and TψC-loop is more important for ScotRNALeu(UAA). We showed that the leucine-specific domain (LSD) of ScoLeuRS could help LeuRS, which originally only leucylates type II tRNALeu, to aminoacylate type I ScotRNALeu(CAA) and identified the crucial amino acid residues at the C-terminus of the LSD to recognize type I ScotRNALeu(CAA). Overall, our findings identified a rare recognition mechanism of LeuRS to tRNALeu.
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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) | 0.000 | 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".