Molecular recognition at cholinergic synapses: acetylcholine<i>versus</i>choline
Bibliographic record
Abstract
KEY POINTS: Neuromuscular acetylcholine (ACh) receptors have a high affinity for the neurotransmitter ACh and a low affinity for its metabolic product choline. At each transmitter binding site three aromatic groups determine affinity, and together provide ∼50% more binding energy for ACh than for choline. Deprotonation of αY190 by a nearby lysine strengthens the interaction between this aromatic ring and both ACh and choline. H-bonds position ACh and choline differently in the aromatic cage to generate the different affinities. ABSTRACT: Acetylcholine (ACh) released at the vertebrate nerve-muscle synapse is hydrolysed rapidly to choline (Cho), so endplate receptors (AChRs) are exposed to high concentrations of both of these structurally related ligands. To understand how these receptors distinguish ACh and Cho, we used single-channel electrophysiology to measure resting affinities (binding free energies) of these and other agonists in adult-type mouse AChRs having a mutation(s) at the transmitter-binding sites. The aromatic rings of αY190, αW149 and αY198 each provide ∼50% less binding energy for Cho compared to ACh. At αY198 a phenylalanine substitution had no effect, but at αY190 this substitution caused a large, agonist-independent loss in binding energy that depended on the presence of αK145. The results suggest that (1) αY190 is deprotonated by αK145 to strengthen the interaction between this benzene ring and the agonist's quaternary ammonium (QA) and (2) AChRs respond strongly to ACh because an H-bond positions the QA to interact optimally with the rings, and weakly to Cho because a different H-bond tethers the ligand to misalign the QA and form weaker interactions with the aromatic groups. The results suggest that the difference in ACh versus Cho binding energies is determined by different ligand positions within a fixed protein structure.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".