Oxygen activation in <scp>NO</scp> synthases: evidence for a direct role of the substrate
Bibliographic record
Abstract
Nitric oxide (NO) and the other reactive nitrogen species (RNOS) play crucial patho-physiological roles at the interface of oxidative stress and signalling processes. In mammals, the NO synthases (NOSs) are the source of these reactive nitrogen species, and so to understand the precise biological role of RNOS and NO requires elucidation of the molecular functioning of NOS. Oxygen activation, which is at the core of NOS catalysis, involves a sophisticated sequence of electron and proton transfers. While electron transfer in NOS has received much attention, the proton transfer processes has been scarcely investigated. Here, we report an original approach that combines fast-kinetic techniques coupled to resonance Raman spectroscopy with the use of synthetic analogues of NOS substrate. We characterise Fe(II)-O2 reaction intermediates in the presence of L-arginine (Arg), alkyl- and aryl-guanidines. The presence of new reaction intermediates, such as ferric haem-peroxide, that was formerly postulated, was tracked by analysing the oxygen activation reaction at different times and with different excitation wavelengths. Our results suggest that Arg is not a proton donor, but indirectly intervenes in oxygen activation mechanism by modulating the distal H-bond network and, in particular, by tuning the position and the role of the distal water molecule. This report supports a catalytic model with two proton transfers in step 1 (Arg hydroxylation) but only one proton transfer in step 2 (N(ω)-hydroxy-L-arginine oxidation).
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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) | 0.000 | 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".