(R)-β-Lysine-modified Elongation Factor P Functions in Translation Elongation
Bibliographic record
Abstract
Post-translational modification of bacterial elongation factor P (EF-P) with ( R )-β-lysine at a conserved lysine residue activates the protein in vivo and increases puromycin reactivity of the ribosome in vitro . The additional hydroxylation of EF-P at the same lysine residue by the YfcM protein has also recently been described. The roles of modified and unmodified EF-P during different steps in translation, and how this correlates to its physiological role in the cell, have recently been linked to the synthesis of polyproline stretches in proteins. Polysome analysis indicated that EF-P functions in translation elongation, rather than initiation as proposed previously. This was further supported by the inability of EF-P to enhance the rate of formation of fMet-Lys or fMet-Phe, indicating that the role of EF-P is not to specifically stimulate formation of the first peptide bond. Investigation of hydroxyl-(β)-lysyl-EF-P showed 30% increased puromycin reactivity but no differences in dipeptide synthesis rates when compared with the β-lysylated form. Unlike disruption of the other genes required for EF-P modification, deletion of yfcM had no phenotypic consequences in Salmonella . Taken together, our findings indicate that EF-P functions in translation elongation, a role critically dependent on post-translational β-lysylation but not hydroxylation. Background: Post-translational modification activates bacterial elongation factor P (EF-P) in several Gram-negative bacteria. Results: The addition of β-lysine alone is sufficient for activation of EF-P to function in translation. Conclusion: Modified EF-P acts by regulating translation of a subset of mRNAs. Significance: EF-P can post-transcriptionally regulate gene expression by controlling translation elongation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".