Kinetic basis for global loss of fidelity arising from mismatches in the P-site codon:anticodon helix
Bibliographic record
Abstract
Faithful decoding of the genetic information by the ribosome relies on kinetically driven mechanisms that promote selection of cognate substrates during elongation. Recently, we have shown that in addition to these kinetically driven mechanisms, the ribosome possesses a post peptidyl transfer quality control system that retrospectively monitors the codon-anticodon interaction in the P site, triggering substantial losses in the specificity of the A site during subsequent tRNA and RF selection when a mistake has occurred. Here, we report a detailed kinetic analysis of tRNA selection in the context of a mismatched P-site codon:anticodon interaction. We observe pleiotropic effects of a P-site mismatch on tRNA selection, such that near-cognate tRNA is processed by the ribosome almost as efficiently as cognate. In particular, after a miscoding event, near-cognate codon-anticodon complexes are stabilized on the ribosome to an extent similar to that observed for cognate ones. Moreover, the two observed forward rates of GTPase activation and accommodation are greatly accelerated (∼10-fold) for near-cognate tRNAs. Because the ensemble of effects of a mismatched P site on substrate selection were found to be different from those reported for other ribosomal perturbations and miscoding agents, we propose that the structural integrity of the mRNA-tRNA helix in the P site provides a distinct molecular switch that dictates the specificity of the A site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".