Insights into the interaction dynamics between volatile anesthetics and tubulin through computational molecular modelling
Why this work is in the frame
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Bibliographic record
Abstract
General anesthetics, able to reversibly suppress all conscious brain activity, have baffled medical science for decades, and little is known about their exact molecular mechanism of action. Given the recent scientific interest in the exploration of microtubules as putative functional targets of anesthetics, and the involvement thereof in neurodegenerative disorders, the present work focuses on the investigation of the interaction between human tubulin and four volatile anesthetics: ethylene, desflurane, halothane and methoxyflurane. Interaction sites on different tubulin isotypes are predicted through docking, along with an estimate of the binding affinity ranking. The analysis is expanded by Molecular Dynamics simulations, where the dimers are allowed to freely interact with anesthetics in the surrounding medium. This allowed for the determination of interaction hotspots on tubulin dimers, which could be linked to different functional consequences on the microtubule architecture, and confirmed the weak, Van der Waals-type interaction, occurring within hydrophobic pockets on the dimer. Both docking and MD simulations highlighted significantly weaker interactions of ethylene, consistent with its far lower potency as a general anesthetic. Overall, simulations suggest a transient interaction between anesthetics and microtubules in general anesthesia, and contact probability analysis shows interaction strengths consistent with the potencies of the four compounds.Communicated by Ramaswamy H. Sarma.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it