The +4G Site in Kozak Consensus Is Not Related to the Efficiency of Translation Initiation
Bibliographic record
Abstract
The optimal context for translation initiation in mammalian species is GCCRCCaugG (where R = purine and "aug" is the initiation codon), with the -3R and +4G being particularly important. The presence of +4G has been interpreted as necessary for efficient translation initiation. Accumulated experimental and bioinformatic evidence has suggested an alternative explanation based on amino acid constraint on the second codon, i.e., amino acid Ala or Gly are needed as the second amino acid in the nascent peptide for the cleavage of the initiator Met, and the consequent overuse of Ala and Gly codons (GCN and GGN) leads to the +4G consensus. I performed a critical test of these alternative hypotheses on +4G based on 34169 human protein-coding genes and published gene expression data. The result shows that the prevalence of +4G is not related to translation initiation. Among the five G-starting codons, only alanine codons (GCN), and glycine codons (GGN) to a much smaller extent, are overrepresented at the second codon, whereas the other three codons are not overrepresented. While highly expressed genes have more +4G than lowly expressed genes, the difference is caused by GCN and GGN codons at the second codon. These results are inconsistent with +4G being needed for efficient translation initiation, but consistent with the proposal of amino acid constraint hypothesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".