Convergence and constraint in eukaryotic release factor 1 (eRF1) domain 1: the evolution of stop codon specificity
Bibliographic record
Abstract
Class 1 release factor in eukaryotes (eRF1) recognizes stop codons and promotes peptide release from the ribosome. The 'molecular mimicry' hypothesis suggests that domain 1 of eRF1 is analogous to the tRNA anticodon stem-loop. Recent studies strongly support this hypothesis and several models for specific interactions between stop codons and residues in domain 1 have been proposed. In this study we have sequenced and identified novel eRF1 sequences across a wide diversity of eukaryotes and re-evaluated the codon-binding site by bioinformatic analyses of a large eRF1 dataset. Analyses of the eRF1 structure combined with estimates of evolutionary rates at amino acid sites allow us to define the residues that are under structural (i.e. those involved in intramolecular interactions) versus non-structural selective constraints. Furthermore, we have re-assessed convergent substitutions in the ciliate variant code eRF1s using maximum likelihood-based phylogenetic approaches. Our results favor the model proposed by Bertram et al. that stop codons bind to three 'cavities' on the protein surface, although we suggest that the stop codon may bind in the opposite orientation to the original model. We assess the feasibility of this alternative binding orientation with a triplet stop codon and the eRF1 domain 1 structures using molecular modeling techniques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".