The C-terminal Helix of Pseudomonas aeruginosa Elongation Factor Ts Tunes EF-Tu Dynamics to Modulate Nucleotide Exchange
Bibliographic record
Abstract
Little is known about the conservation of critical kinetic parameters and the mechanistic strategies of elongation factor (EF) Ts-catalyzed nucleotide exchange in EF-Tu in bacteria and particularly in clinically relevant pathogens. EF-Tu from the clinically relevant pathogen Pseudomonas aeruginosa shares over 84% sequence identity with the corresponding elongation factor from Escherichia coli Interestingly, the functionally closely linked EF-Ts only shares 55% sequence identity. To identify any differences in the nucleotide binding properties, as well as in the EF-Ts-mediated nucleotide exchange reaction, we performed a comparative rapid kinetics and mutagenesis analysis of the nucleotide exchange mechanism for both the E. coli and P. aeruginosa systems, identifying helix 13 of EF-Ts as a previously unnoticed regulatory element in the nucleotide exchange mechanism with species-specific elements. Our findings support the base side-first entry of the nucleotide into the binding pocket of the EF-Tu·EF-Ts binary complex, followed by displacement of helix 13 and rapid binding of the phosphate side of the nucleotide, ultimately leading to the release of EF-Ts.
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) | 0.000 | 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 | 0.000 |
| 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".