Substrate and pH-Dependent Kinetic Profile of 3-Mercaptopropionate Dioxygenase from <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Thiol dioxygenases catalyze the synthesis of sulfinic acids in a range of organisms from bacteria to mammals. A thiol dioxygenase from the bacterium Pseudomonas aeruginosa oxidizes both 3-mercaptopropionic acid and cysteine, with a ∼70 fold preference for 3-mercaptopropionic acid over all pHs. This substrate reactivity is widened compared to other thiol dioxygenases and was exploited in this investigation of the residues important for activity. A simple model incorporating two protonation events was used to fit profiles of the Michaelis-Menten parameters determined at different pH values for both substrates. The pKs determined using plots of k(cat)/Km differ at low pH, but not in a way easily attributable to protonation of the substrate alone and share a common value at higher pH. Plots of k(cat) versus pH are also quite different at low pH showing the monoprotonated ES complexes with 3-mercaptopropionic acid and cysteine have different pKs. At higher pH, k(cat) decreases sigmoidally with a similar pK regardless of substrate. Loss of reactivity at high pH is attributed to deprotonation of tyrosine 159 and its influence on dioxygen binding. A mechanism is proposed by which deprotonation of tyrosine 159 both blocks oxygen binding and concomitantly promotes cystine formation. Finally, the role of tyrosine 159 was further probed by production of a G95C variant that is able to form a cysteine-tyrosine crosslink homologous to that found in mammalian cysteine dioxygenases. Activity of this variant is severely impaired. Crystallography shows that when un-crosslinked, the cysteine thiol excludes tyrosine 159 from its native position, while kinetic analysis shows that the thioether bond impairs reactivity of the crosslinked form.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 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 teacher head, 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".