The Influence of Anticodon-Codon Interactions and Modified Bases on Codon Usage Bias in Bacteria
Bibliographic record
Abstract
Most transfer RNAs (tRNAs) can translate more than one synonymous codon, and most codons can be translated by more than one isoacceptor tRNA. The rates of translation of synonymous codons are dependent on the concentrations of the tRNAs and on the rates of pairing of each anticodon-codon combination. Translational selection causes a significant bias in codon frequencies in highly expressed genes in most bacteria. By comparing codon frequencies in high and low-expression genes, we determine which codons are preferred for each amino acid in a large sample of bacterial genomes. We relate this to the number of copies of each tRNA gene in each genome. In two-codon families, preferred codons have Watson-Crick pairs (GC and AU) between the third codon base and the wobble base of the anticodon rather than GU pairs. This suggests that these combinations are more rapidly recognized by the ribosome. In contrast, in four-codon families, preferred codons do not correspond to Watson-Crick rules. In some cases, a wobble-U tRNA can pair with all four codons. In these cases, A and U codons are preferred over G and C. This indicates that the nonstandard UU combination appears to be translated surprisingly well. Differences in modified bases at the wobble position of the anticodon appear to be responsible for the differences in behavior of tRNAs in two- and four-codon families. We discuss the way changes in the bases in the anticodon influence both the speed and the accuracy of translation. The number of tRNA gene copies and the strength of translational selection correlate with the growth rate of the organism, as we would expect if the primary cause of translational selection in bacteria is the requirement to optimize the speed of protein production.
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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".