Coupling structure with function in acid‐sensing ion channels: challenges in pursuit of proton sensors
Bibliographic record
Abstract
Acid-sensing ion channels (ASICs) are a class of trimeric cation-selective ion channels activated by changes in pH within the physiological range. They are widely expressed in the central and peripheral nervous systems where they participate in a range of physiological and pathophysiological situations such as learning and memory, pain sensation, fear and anxiety, substance abuse and cell death. ASICs are localized to cell bodies and dendrites, including the postsynaptic density, and within the last 5 years several examples of proton-evoked ASIC excitatory postsynaptic currents have emerged. Thus, ASICs have become bona fide neurotransmitter-gated ion channels, activated by the smallest neurotransmitter possible: protons. Here we review how protons are thought to drive the conformational changes associated with ASIC activation and desensitization. In particular, we weigh the evidence for and against the so-called 'acidic pocket' being a vital proton sensor and discuss the emerging role of the β11-12 linker as a desensitization switch or 'molecular clutch'. We also examine how proton-induced conformational changes pose unique challenges to classical molecular dynamics simulations, as well as some possible solutions. Given the emergence of new methodologies and structures, the coming years will probably see many advances in the study of acid-sensing ion channels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".