Mapping the sevoflurane‐binding sites of calmodulin
Bibliographic record
Abstract
General anesthetics, with sevoflurane (SF) being the first choice inhalational anesthetic agent, provide reversible, broad depressor effects on the nervous system yet have a narrow margin of safety. As characterization of low-affinity binding interactions of volatile substances is exceptionally challenging with the existing methods, none of the numerous cellular targets proposed as chief protagonists in anesthesia could yet be confirmed. The recognition that most critical functions modulated by volatile anesthetics are under the control of intracellular Ca(2+) concentration, which in turn is primarily regulated by calmodulin (CaM), motivated us for characterization of the SF-CaM interaction. Solution NMR (Nuclear Magnetic Resonance) spectroscopy was used to identify SF-binding sites using chemical shift displacement, NOESY and heteronuclear Overhauser enhancement spectroscopy (HOESY) experiments. Binding affinities were measured using ITC (isothermal titration calorimetry). SF binds to both lobes of (Ca(2+))4-CaM with low mmol/L affinity whereas no interaction was observed in the absence of Ca(2+). SF does not affect the calcium binding of CaM. The structurally closely related SF and isoflurane are shown to bind to the same clefts. The SF-binding clefts overlap with the binding sites of physiologically relevant ion channels and bioactive small molecules, but the binding affinity suggests it could only interfere with very weak CaM targets.
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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) | 0.002 | 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".