Mechanism of the inhibition of calmodulin‐dependent neuronal nitric oxide synthase by flaxseed protein hydrolysates
Bibliographic record
Abstract
Abstract This work was aimed at producing potential nutraceutical peptides from flaxseed protein hydrolysate that can bind to calmodulin (CaM) and inhibit the activity of CaM‐dependent neuronal nitric oxide synthase (nNOS), an enzyme that has been implicated in some forms of human diseases. Flaxseed protein isolate was hydrolyzed with alcalase, and the resultant protein hydrolysate was passed through a 1000‐Da M.W. cut‐off membrane to isolate low‐M.W. peptides. The permeate from the membrane was loaded onto a cation‐exchange column, and adsorbed peptides were separated into fractions I and II that had a content of 42 and 51% basic amino acids, respectively. Kinetic analyses showed that both fractions were capable of binding to CaM, which led to reductions in the activity of nNOS; the inhibition constant (Kj) was 5.97 and 2.55 mg/mL for fractions I and II, respectively. Double reciprocal plots showed that the mode of enzyme inhibition was mostly noncompetitive. Estimation of nNOS structure by fluorescence spectroscopy indicated that binding of the peptides to CaM led to a gradual unfolding of enzyme structure as levels of the fractions were increased. We concluded that the flaxseed protein‐derived peptides may be used as ingredients for the formulation of therapeutic foods.
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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".