The determination of tRNA<sup>Leu</sup> recognition nucleotides for <i>Escherichia coli</i> L/F transferase
Bibliographic record
Abstract
Escherichia coli leucyl/phenylalanyl-tRNA protein transferase catalyzes the tRNA-dependent post-translational addition of amino acids onto the N-terminus of a protein polypeptide substrate. Based on biochemical and structural studies, the current tRNA recognition model by L/F transferase involves the identity of the 3' aminoacyl adenosine and the sequence-independent docking of the D-stem of an aminoacyl-tRNA to the positively charged cluster on L/F transferase. However, this model does not explain the isoacceptor preference observed 40 yr ago. Using in vitro-transcribed tRNA and quantitative MALDI-ToF MS enzyme activity assays, we have confirmed that, indeed, there is a strong preference for the most abundant leucyl-tRNA, tRNA(Leu) (anticodon 5'-CAG-3') isoacceptor for L/F transferase activity. We further investigate the molecular mechanism for this preference using hybrid tRNA constructs. We identified two independent sequence elements in the acceptor stem of tRNA(Leu) (CAG)-a G₃:C₇₀ base pair and a set of 4 nt (C₇₂, A₄:U₆₉, C₆₈)-that are important for the optimal binding and catalysis by L/F transferase. This maps a more specific, sequence-dependent tRNA recognition model of L/F transferase than previously proposed.
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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 | 0.001 |
| 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".