ΔF508-CFTR Modulator Screen Based on Cell Surface Targeting of a Chimeric Nucleotide Binding Domain 1 Reporter
Bibliographic record
Abstract
The most common cystic fibrosis–causing mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) is deletion of phenylalanine at residue 508 (∆F508). The ∆F508 mutation impairs folding of nucleotide binding domain 1 (NBD1) and interfacial interactions of NBD1 and the membrane spanning domains. Here, we report a domain-targeted screen to identify ∆F508-CFTR modulators that act on NBD1. A biochemical screen for ΔF508-NBD1 cell surface expression was done in Madin–Darby canine kidney cells expressing a chimeric reporter consisting of ΔF508-NBD1, the CD4 transmembrane domain, and an extracellular horseradish peroxidase (HRP) reporter. Using a luminescence readout of HRP activity, the screen was robust with a Z′ factor of 0.7. The screening of ~20,000 synthetic small molecules allowed the identification of compounds from four chemical classes that increased ∆F508-NBD1 cell surface expression by up to 4-fold; for comparison, a 12-fold increased cell surface expression was found for a wild-type NBD1 chimera. While the compounds were inactive as correctors of full-length ΔF508-CFTR, several carboxamide-benzothiophenes had potentiator activity with low micromolar EC 50 . Interestingly, the potentiators did not activate G551D or wild-type CFTR. Our results provide a proof of concept for a cell-based NBD1 domain screen to identify ∆F508-CFTR modulators that target the NBD1 domain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".