FRET analysis of Calmodulin Binding to Nitric Oxide Synthase Peptides and Enzymes
Bibliographic record
Abstract
Calmodulin (CaM) is a ubiquitous Ca 2+ ‐sensor protein that binds and activates the Nitric Oxide Synthase (NOS) isozymes. Although the structure of CaM has been determined when bound to a peptide derived from the CaM‐binding domain of endothelial NOS, the conformation of CaM when bound to the three mammalian NOS holoenzymes has yet to be determined. Förster Resonance Energy Transfer (FRET) is a useful method for the determination of the distance between two different fluorescently labeled residues separated by ~10 to ~100 Å. The goal of our research is to determine the conformation of CaM when bound to all three NOS isoforms through the use of FRET. We have produced a double cysteine CaM mutant (T34C/T110C) using site‐directed mutagenesis for these FRET measurements. The NOS enzymes contain three cofactors (heme, FMN, and FAD) that absorb light strongly between 250 and 500 nm. In order to avoid undesired quenching, far‐red shifted fluorescent dyes (Alexa Fluor 546 C 5 ‐maleimide and DABMI) were chosen so that they can be selectively excited in the presence of the NOS cofactors. FRET studies were performed on the fluorescently labeled CaM with stoichiometric amounts of synthetic CaM‐binding domain NOS peptides and enzymes. These studies will provide a better understanding of the inherent differences between the Ca 2+ ‐dependent and Ca 2+ ‐independent binding of CaM to the mammalian NOS isozymes. This work is supported by NSERC.
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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) | 0.001 | 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".