Regulation of interdomain electron transfer in the NOS output state for NO production
Bibliographic record
Abstract
There is still much that is unknown about how nitric oxide (NO) biosynthesis by NO synthase (NOS) isoform is tightly regulated at the molecular level. This is remarkable because deviated NO production in vivo has been implicated in an increasing number of diseases that currently lack effective treatments, including stroke and cancer. Given the significant public health burden of these diseases, the NOS enzyme family is a key target for development of new pharmaceuticals. Three NOS isoforms, inducible, endothelial and neuronal NOS (iNOS, eNOS and nNOS, respectively), achieve their key biological functions via stringent regulations of interdomain electron transfer (IET) processes. Unlike iNOS, eNOS and nNOS isoforms are controlled by calmodulin (CaM) binding through facilitating catalytically significant IET processes. The CaM-modulated NOS output state is an IET-competent complex between the flavin mononucleotide (FMN) domain and the catalytic heme domain. The output state facilitates the catalytically essential FMN-heme IET, and thereby enables NO production by NOS. Due to lack of reliable techniques for specifically determining the inter-domain FMN-heme interactions and their direct effects on the catalytic heme center, the molecular mechanism that underlies the output state formation remains elusive. The recent developments in our understanding of mechanisms of the NOS output state formation that are driven by a combination of molecular biology, laser flash photolysis, and spectroscopic techniques are the subject of this perspective.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".