Facilitating Structure-Function Studies of CFTR Modulator Sites with Efficiencies in Mutagenesis and Functional Screening
Bibliographic record
Abstract
There are nearly 2000 mutations in the CFTR gene associated with cystic fibrosis disease, and to date, the only approved drug, Kalydeco, has been effective in rescuing the functional expression of a small subset of these mutant proteins with defects in channel activation. However, there is currently an urgent need to assess other mutations for possible rescue by Kalydeco, and further, definition of the binding site of such modulators on CFTR would enhance our understanding of the mechanism of action of such therapeutics. Here, we describe a simple and rapid one-step PCR-based site-directed mutagenesis method to generate mutations in the CFTR gene. This method was used to generate CFTR mutants bearing deletions (p.Gln2_Trp846del, p.Ser700_Asp835del, p.Ile1234_Arg1239del) and truncation with polyhistidine tag insertion (p.Glu1172-3Gly-6-His*), which either recapitulate a disease phenotype or render tools for modulator binding site identification, with subsequent evaluation of drug responses using a high-throughput (384-well) membrane potential-sensitive fluorescence assay of CFTR channel activity within a 1 wk time frame. This proof-of-concept study shows that these methods enable rapid and quantitative comparison of multiple CFTR mutants to emerging drugs, facilitating future large-scale efforts to stratify mutants according to their "theratype" or most promising targeted therapy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".