Investigating the in vivo activity of the DeaD protein using protein–protein interactions and the translational activity of structured chloramphenicol acetyltransferase mRNAs
Bibliographic record
Abstract
Here, we report the use of an in vivo protein-protein interaction detection approach together with focused follow-up experiments to study the function of the DeaD protein in Escherichia coli. In this method, functions are assigned to proteins based on the interactions they make with others in the living cell. The assigned functions are further confirmed using follow-up experiments. The DeaD protein has been characterized in vitro as a putative prokaryotic factor required for the formation of translation initiation complexes on structured mRNAs. Although the RNA helicase activity of DeaD has been demonstrated in vitro, its in vivo activity remains controversial. Here, using a method called sequential peptide affinity (SPA) tagging, we show that DeaD interacts with certain ribosomal proteins as well as a series of other nucleic acid binding proteins. Focused follow-up experiments provide evidence for the mRNA helicase activity of the DeaD protein complex during translation initiation. DeaD overexpression compensates for the reduction of the translation activity caused by a structure placed at the initiation region of a chloramphenicol acetyltransferase gene (cat) used as a reporter. Deletion of the deaD gene, encoding DeaD, abolishes the translation activity of the mRNA with an inhibitory structure at its initiation region. Increasing the growth temperature disrupts RNA secondary structures and bypasses the DeaD requirement. These observations suggest that DeaD is involved in destabilizing mRNA structures during translation initiation. This study also provides further confirmation that large-scale protein-protein interaction data can be suitable to study protein functions in E. coli.
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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) | 0.001 | 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".