Modulating Mistranslation Potential of tRNASer in<i>Saccharomyces cerevisiae</i>
Bibliographic record
Abstract
Abstract Mistranslation, incorporating an amino acid not specified by the “standard” genetic code, has applications in research and synthetic biology. Since mistranslation is toxic, its level must be modulated. Using a serine tRNA with a proline anticodon, we identify... Transfer RNAs (tRNAs) read the genetic code, translating nucleic acid sequence into protein. For tRNASer the anticodon does not specify its aminoacylation. For this reason, mutations in the tRNASer anticodon can result in amino acid substitutions, a process called mistranslation. Previously, we found that tRNASer with a proline anticodon was lethal to cells. However, by incorporating secondary mutations into the tRNA, mistranslation was dampened to a nonlethal level. The goal of this work was to identify second-site substitutions in tRNASer that modulate mistranslation to different levels. Targeted changes to putative identity elements led to total loss of tRNA function or significantly impaired cell growth. However, through genetic selection, we identified 22 substitutions that allow nontoxic mistranslation. These secondary mutations are primarily in single-stranded regions or substitute G:U base pairs for Watson–Crick pairs. Many of the variants are more toxic at low temperature and upon impairing the rapid tRNA decay pathway. We suggest that the majority of the secondary mutations affect the stability of the tRNA in cells. The temperature sensitivity of the tRNAs allows conditional mistranslation. Proteomic analysis demonstrated that tRNASer variants mistranslate to different extents with diminished growth correlating with increased mistranslation. When combined with a secondary mutation, other anticodon substitutions allow serine mistranslation at additional nonserine codons. These mistranslating tRNAs have applications in synthetic biology, by creating “statistical proteins,” which may display a wider range of activities or substrate specificities than the homogenous form.
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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".