Nanomechanics combined with HDX reveals allosteric drug binding sites of CFTR NBD1
Bibliographic record
Abstract
Cystic fibrosis (CF) is a frequent genetic disease in Caucasians that is caused by the deletion of F508 (ΔF508) in the nucleotide binding domain 1 (NBD1) of the CF transmembrane conductance regulator (CFTR). The ΔF508 compromises the folding energetics of the NBD1, as well as the folding of three other CFTR domains. Combination of FDA approved corrector molecules can efficiently but incompletely rescue the ΔF508-CFTR folding and stability defect. Thus, new pharmacophores that would reinstate the wild-type-like conformational stability of the ΔF508-NBD1 would be highly beneficial. The most prominent molecule, 5-bromoindole-3-acetic acid (BIA) that can thermally stabilize the NBD1 has low potency and efficacy. To gain insights into the NBD1 (un)folding dynamics and BIA binding site localization, we combined molecular dynamics (MD) simulations, atomic force spectroscopy (AFM) and hydrogen-deuterium exchange (HDX) experiments. We found that the NBD1 α-subdomain with three adjacent strands from the β-subdomain plays an important role in early folding steps, when crucial non-native interactions are formed via residue F508. Our AFM and HDX experiments showed that BIA associates with this α-core region and increases the resistance of the ΔF508-NBD1 against mechanical unfolding, a phenomenon that could be exploited in future developments of folding correctors.
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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) | 0.002 | 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".