Computational modelling of specific serotonin reuptake inhibitor inhibition on the TREK-2 channel
Bibliographic record
Abstract
Abstract The TREK-2 is a mechanosensitive potassium channel in the two-pore (K2P) potassium channel subfamily. Recent studies of the TREK-2 channel with norfluoxetine reveal that norfluoxetine stabilises a conformation with a lower open probability and disrupts channel gating through a selectivity filter. In addition, multiple specific serotonin reuptake inhibitors (SSRIs) have previously been shown to inhibit the TREK channels subfamily. However, the mechanism of lipid-like SSRI inhibition to the TREK-2 channel is currently unclear. Using molecular dynamic simulation, we show that fluoxetine and escitalopram share the same binding pocket on the TREK-2 channel. We show that fluoxetine inhibits the TREK-2 channel using POPC lipid and directly disrupts the channel gating at the selectivity filter, while escitalopram is a traditional pore blocker, which also disrupts the selectivity filter gating but without POPC dependent inhibition. In addition, we show that both fluoxetine and escitalopram prevent a down-to-up transition when the pressure is applied to the system, showing a conserved mechanism of TREK-2 inhibition. Together, our work reveals mechanistic insight into TREK-2 channel inhibition by lipid-like antidepressants, which could further shed light on rational drug design in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".