The cost of wobble translation in fungal mitochondrial genomes: integration of two traditional hypotheses
Bibliographic record
Abstract
BACKGROUND: Fungal and animal mitochondrial genomes typically have one tRNA for each synonymous codon family. The codon-anticodon adaptation hypothesis predicts that the wobble nucleotide of a tRNA anticodon should evolve towards maximizing Watson-Crick base pairing with the most frequently used codon within each synonymous codon family, whereas the wobble versatility hypothesis argues that the nucleotide at the wobble site should be occupied by a nucleotide most versatile in wobble pairing, i.e., the tRNA wobble nucleotide should be G for NNY codon families, and U for NNR and NNN codon families (where Y stands for C or U, R for A or G and N for any nucleotide). RESULTS: We here integrate these two traditional hypotheses on tRNA anticodons into a unified model based on an analysis of the wobble costs associated with different wobble base pairs. This novel approach allows the relative cost of wobble pairing to be qualitatively evaluated. A comprehensive study of 36 fungal genomes suggests very different costs between two kinds of U:G wobble pairs, i.e., (1) between a G at the wobble site of a tRNA anticodon and a U at the third codon position (designated MU3:G) and (2) between a U at the wobble site of a tRNA anticodon and a G at the third codon position (designated MG3:U). CONCLUSION: In general, MU3:G is much smaller than MG3:U, suggesting no selection against U-ending codons in NNY codon families with a wobble G in the tRNA anticodon but strong selection against G-ending codons in NNR codon families with a wobble U at the tRNA anticodon. This finding resolves several puzzling observations in fungal genomics and corroborates previous studies showing that U3:G wobble is energetically more favorable than G3:U wobble.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".