Integrated in vivo and in vitro nascent chain profiling reveals widespread translational pausing
Bibliographic record
Abstract
Although the importance of the nonuniform progression of elongation in translation is well recognized, there have been few attempts to explore this process by directly profiling nascent polypeptides, the relevant intermediates of translation. Such approaches will be essential to complement other approaches, including ribosome profiling, which is extremely powerful but indirect with respect to the actual translation processes. Here, we use the nascent polypeptide's chemical trait of having a covalently attached tRNA moiety to detect translation intermediates. In a case study, Escherichia coli SecA was shown to undergo nascent polypeptide-dependent translational pauses. We then carried out integrated in vivo and in vitro nascent chain profiling (iNP) to characterize 1,038 proteome members of E. coli that were encoded by the first quarter of the chromosome with respect to their propensities to accumulate polypeptidyl-tRNA intermediates. A majority of them indeed undergo single or multiple pauses, some occurring only in vitro, some occurring only in vivo, and some occurring both in vivo and in vitro. Thus, translational pausing can be intrinsically robust, subject to in vivo alleviation, or require in vivo reinforcement. Cytosolic and membrane proteins tend to experience different classes of pauses; membrane proteins often pause multiple times in vivo. We also note that the solubility of cytosolic proteins correlates with certain categories of pausing. Translational pausing is widespread and diverse in nature.
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".