A Catalytic Loop within Pseudomonas aeruginosa Exotoxin A Modulates Its Transferase Activity
Bibliographic record
Abstract
Mutagenesis techniques were used to replace two loop regions within the catalytic domain of Pseudomonas aeruginosa exotoxin A (ETA) with functionally silent polyglycine loops. The loop mutant proteins, designated polyglycine Loops N and C, were both less active than the wild-type enzyme. However, the polyglycine Loop C mutant protein, replaced with the Gly(483)-Gly(490) loop, showed a much greater loss of enzymatic activity than the polyglycine Loop N protein. The former mutant enzyme exhibited an 18,000-fold decrease in catalytic turnover number (k(cat)), with only a marginal effect on the K(m) value for NAD(+) and the eukaryotic elongation factor-2 binding constant. Furthermore, alanine-scanning mutagenesis of this active-site loop region revealed the specific pattern of a critical region for enzymatic activity. Binding and kinetic data suggest that this loop modulates the transferase activity between ETA and eukaryotic elongation factor-2 and may be responsible for stabilization of the transition state for the reaction. Sequence alignment and molecular modeling also identified a similar loop within diphtheria toxin, a functionally and structurally related class A-B toxin. Based on these results and the similarities between ETA and diphtheria toxin, we propose that this catalytic subregion represents the first report of a diphthamide-specific ribosyltransferase structural motif. We expect these findings to further the development of pharmaceuticals designed to prevent ETA toxicity by disrupting the stabilization of the transition state during the ADP-ribose transfer event.
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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 | 0.000 |
| 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".