State-dependent Inhibition of Cystic Fibrosis Transmembrane Conductance Regulator Chloride Channels by a Novel Peptide Toxin
Bibliographic record
Abstract
Peptide toxins from animal venom have been used for many years for the identification and study of cation-permeable ion channels. However, no peptide toxins have been identified that interact with known anion-selective channels, including cystic fibrosis transmembrane conductance regulator (CFTR), the protein defective in cystic fibrosis and a member of the ABC transporter superfamily. Here, we describe the identification and initial characterization of a novel 3.7-kDa peptide toxin, GaTx1, which is a potent and reversible inhibitor of CFTR, acting from the cytoplasmic side of the membrane. Thus, GaTx1 is the first peptide toxin identified that inhibits a chloride channel of known molecular identity. GaTx1 exhibited high specificity, showing no effect on a panel of nine transport proteins, including Cl(-) and K(+) channels, and ABC transporters. GaTx1-mediated inhibition of CFTR channel activity is strongly state-dependent; both potency and efficacy are reduced under conditions of elevated [ATP], suggesting that GaTx1 may function as a non-competitive inhibitor of ATP-dependent channel gating. This tool will allow the application of new quantitative approaches to study CFTR structure and function, particularly with respect to the conformational changes that underlie transitions between open and closed states.
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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 | 0.000 |
| 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 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".