A novel fluorescent reporter sensitive to serine mis-incorporation
Bibliographic record
Abstract
High-fidelity translation was considered a requirement for living cells. The frozen accident theory suggested that any deviation from the standard genetic code should result in the production of so much mis-made and non-functional proteins that cells cannot remain viable. Studies in bacterial, yeast, and mammalian cells show that significant levels of mistranslation (1–10% per codon) can be tolerated or even beneficial under conditions of oxidative stress. Single tRNA mutants, which occur naturally in the human population, can lead to amino acid mis-incorporation at a codon or set of codons. The rate or level of mistranslation can be difficult or impossible to measure in live cells. We developed a novel red fluorescent protein reporter that is sensitive to serine (Ser) mis-incorporation at proline (Pro) codons. The mCherry Ser151Pro mutant is efficiently produced in Escherichia coli but non-fluorescent. We demonstrated in cells and with purified mCherry protein that the fluorescence of mCherry Ser151Pro is rescued by two different tRNASer gene variants that were mutated to contain the Pro (UGG) anticodon. Ser mis-incorporation was confirmed by mass spectrometry. Remarkably, E. coli tolerated mistranslation rates of ~10% per codon with negligible reduction in growth rate. Conformational sampling simulations revealed that the Ser151Pro mutant leads to significant changes in the conformational freedom of the chromophore precursor, which is indicative of a defect in chromophore maturation. Together our data suggest that the mCherry Ser151 mutants may be used to report Ser mis-incorporation at multiple other codons, further expanding the ability to measure mistranslation in living cells.
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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 | 0.001 | 0.001 |
| 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".