Multiscale molecular dynamics simulations predict arachidonic acid binding sites in human ASIC1a and ASIC3 transmembrane domains
Bibliographic record
Abstract
Acid-sensing ion channels (ASICs) play important roles in inflammatory pathways by conducting ions across the neuronal membrane in response to proton binding under acidic conditions. Recent studies have shown that ASICs can be modulated by arachidonic acid (AA), and, in the case of the ASIC3 subtype, even activated by AA at physiological pH. However, the mechanism by which these fatty acids act on the channel is still unknown. Here, we have used multiscale molecular dynamics simulations to predict a putative, general binding region of AA to models of the human ASIC protein. We have identified, in agreement with recent studies, residues in the outer leaflet transmembrane region which interact with AA. In addition, despite their similar modulation, we observe subtle differences in the AA interaction pattern between human ASIC1a and human ASIC3, which can be reversed by mutating three key residues at the outer leaflet portion of TM1. We further probed interactions with these residues in hASIC3 using atomistic simulations and identified possible AA coordinating interactions; salt bridge interactions of AA with R65hASIC3 and R68hASIC3 and AA tail interactions with the Y58hASIC3 aromatic ring. We have shown that longer fatty acid tails with more double bonds have increased relative occupancy in this region of the channel, a finding supported by recent functional studies. We further proposed that the modulatory effect of AA on ASIC does not result from changes in local membrane curvature. Rather, we speculate that it may occur through structural changes to the ion channel upon AA binding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".