A Major Controversy in Codon-Anticodon Adaptation Resolved by a New Codon Usage Index
Bibliographic record
Abstract
Two alternative hypotheses attribute different benefits to codon-anticodon adaptation. The first assumes that protein production is rate limited by both initiation and elongation and that codon-anticodon adaptation would result in higher elongation efficiency and more efficient and accurate protein production, especially for highly expressed genes. The second claims that protein production is rate limited only by initiation efficiency but that improved codon adaptation and, consequently, increased elongation efficiency have the benefit of increasing ribosomal availability for global translation. To test these hypotheses, a recent study engineered a synthetic library of 154 genes, all encoding the same protein but differing in degrees of codon adaptation, to quantify the effect of differential codon adaptation on protein production in Escherichia coli. The surprising conclusion that "codon bias did not correlate with gene expression" and that "translation initiation, not elongation, is rate-limiting for gene expression" contradicts the conclusion reached by many other empirical studies. In this paper, I resolve the contradiction by reanalyzing the data from the 154 sequences. I demonstrate that translation elongation accounts for about 17% of total variation in protein production and that the previous conclusion is due to the use of a codon adaptation index (CAI) that does not account for the mutation bias in characterizing codon adaptation. The effect of translation elongation becomes undetectable only when translation initiation is unrealistically slow. A new index of translation elongation ITE is formulated to facilitate studies on the efficiency and evolution of the translation machinery.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.009 |
| 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".