Perspectives and Insights into the Competition for Aminoacyl-tRNAs between the Translational Machinery and for tRNA Dependent Non-Ribosomal Peptide Bond Formation
Bibliographic record
Abstract
Aminoacyl-tRNA protein transferases catalyze the transfer of amino acids from aminoacyl-tRNAs to polypeptide substrates. Different forms of these enzymes are found in the different kingdoms of life and have been identified to be central to a wide variety of cellular processes. L/F-transferase is the sole member of this class of enzyme found in Escherichia coli and catalyzes the transfer of leucine to the N-termini of proteins which result in the targeted degradation of the modified protein. Recent investigations on the tRNA specificity of L/F-transferase have revealed the unique recognition nucleotides for a preferred Leu-tRNA(Leu) isoacceptor substrate. In addition to discussing this tRNA selectivity by L/F-transferase, we present and discuss a hypothesis and its implications regarding the apparent competition for this aminoacyl-tRNA between L/F-transferase and the translational machinery. Our discussion reveals a hypothetical involvement of the bacterial stringent response that occurs upon amino acid limitation as a potential cellular event that may reduce this competition and provide the opportunity for L/F-transferase to readily increase its access to the pool of aminoacylated 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".