Voltammetric Studies of the Catalytic Mechanism of the Respiratory Nitrate Reductase from <i>Escherichia coli</i>: How Nitrate Reduction and Inhibition Depend on the Oxidation State of the Active Site
Bibliographic record
Abstract
The respiratory molybdoenzyme nitrate reductase (NarGHI) from Escherichia coli has been studied by protein film voltammetry, with the enzyme adsorbed on a rotating disk pyrolytic graphite edge (PGE) electrode. Catalytic voltammograms for nitrate reduction show a complex wave consisting of two components that vary with pH, nitrate concentration, and the presence of inhibitors. At micromolar levels of nitrate, the activity reaches a maximum value at approximately -25 mV and then decreases as the potential becomes more negative. As the nitrate concentration is raised, the activity at more negative potentials increases and eventually becomes the dominant feature at millimolar concentrations. This leads to the hypothesis that nitrate binds more tightly to Mo(V) than Mo(IV), so that low levels of nitrate are more effectively reduced at a higher potential despite the lower driving force. However, an alternative interpretation, that nitrate binding is affected by a change in the redox state of the pterin, cannot be ruled out. This proposal, implicating a specific redox transition at the active site, is supported by experiments carried out using the inhibitors azide and thiocyanate. Azide is the stronger inhibitor of the two, and each inhibitor shows two inhibition constants, one at high potential and one at low potential, both of which are fully competitive with nitrate; closer analysis reveals that the inhibitors act preferentially upon the catalytic activity at high potential. The unusual potential dependence therefore derives from the weaker binding of nitrate or the inhibitors to a more reduced state of the active site. The possible manifestation of these characteristics in vivo has interesting implications for the bioenergetics of 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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".