Translation Initiation Rate Determines the Impact of Ribosome Stalling on Bacterial Protein Synthesis
Bibliographic record
Abstract
Ribosome stalling during translation can be caused by a number of characterized mechanisms. However, the impact of elongation stalls on protein levels is variable, and the reasons for this are often unclear. To investigate this relationship, we examined the bacterial translation elongation factor P (EF-P), which plays a critical role in rescuing ribosomes stalled at specific amino acid sequences including polyproline motifs. In previous proteomic analyses of both Salmonella and Escherichia coli efp mutants, it was evident that not all proteins containing a polyproline motif were dependent on EF-P for efficient expression in vivo. The α- and β-subunits of ATP synthase, AtpA and AtpD, are translated from the same mRNA transcript, and both contain a PPG motif; however, proteomic analysis revealed that AtpD levels are strongly dependent on EF-P, whereas AtpA levels are independent of EF-P. Using these model proteins, we systematically determined that EF-P dependence is strongly influenced by elements in the 5'-untranslated region of the mRNA. By mutating either the Shine-Dalgarno sequence or the start codon, we find that EF-P dependence correlates directly with the rate of translation initiation where strongly expressed proteins show the greatest dependence on EF-P. Our findings demonstrate that polyproline-induced stalls exert a net effect on protein levels only if they limit translation significantly more than initiation. This model can be generalized to explain why sequences that induce pauses in translation elongation to, for example, facilitate folding do not necessarily exact a penalty on the overall production of the protein.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".