Latent Specificity of Molecular Recognition in Sodium Channels Engineered To Discriminate between Two “Indistinguishable” μ-Conotoxins
Bibliographic record
Abstract
mu-Conotoxins (mu-CTX) are potent oligopeptide blockers of sodium channels. The best characterized forms of mu-CTX, GIIIA and GIIIB, have similar primary and three-dimensional structures and comparable potencies (IC(50) approximately 30 nM) for block of wild-type skeletal muscle Na(+) channels. The two toxins are thus considered to be indistinguishable by their target channels. We have found mutations in the domain II pore region (D762K and E765K) that decrease GIIIB blocking affinity approximately 200-fold, but reduce GIIIA affinity by only approximately 4-fold, compared with wild-type channels. Synthetic mu-CTX GIIIA mutants reveal that the critical residue for differential recognition is at position 14, the site of the only charge difference between the two toxin isoforms. Therefore, engineered Na(+) channels, but not wild-type channels, can discriminate between two highly homologous conotoxins. Latent specificity of toxin-channel interactions, such as that revealed here, is a principle worthy of exploitation in the design and construction of improved biosensors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".