Structural mechanisms for α-conotoxin activity at the human α3β4 nicotinic acetylcholine receptor
Bibliographic record
Abstract
Nicotinic acetylcholine receptors (nAChR) are therapeutic targets for a range of human diseases. α-Conotoxins are naturally occurring peptide antagonists of nAChRs that have been used as pharmacological probes and investigated as drug leads for nAChR related disorders. However, α-conotoxin interactions have been mostly characterised at the α7 and α3β2 nAChRs, with interactions at other subtypes poorly understood. This study provides novel structural insights into the molecular basis for α-conotoxin activity at α3β4 nAChR, a therapeutic target where subtype specific antagonists have potential to treat nicotine addiction and lung cancer. A co-crystal structure of α-conotoxin LsIA with Lymnaea stagnalis acetylcholine binding protein guided the design and functional characterisations of LsIA analogues that identified the minimum pharmacophore regulating α3β4 antagonism. Interactions of the LsIA R10F with β4 K57 and the conserved -NN- α-conotoxin motif with β4 I77 and I109 conferred α3β4 activity to the otherwise inactive LsIA. Using these structural insights, we designed LsIA analogues with α3β4 activity. This new understanding of the structural basis of protein-protein interactions between α-conotoxins and α3β4 may help rationally guide the development of α3β4 selective antagonists with therapeutic potential.
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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 | 0.000 |
| 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 | 0.001 |
| 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".