Comprehensive mapping of Cystic Fibrosis mutations to CFTR protein identifies mutation clusters and molecular docking predicts corrector binding site
Bibliographic record
Abstract
Abstract Background Cystic Fibrosis (CF) is caused by mutations in the CFTR gene, of which over 2000 have been reported to date. Mutations have yet to be analyzed in aggregate to assess their distribution across the tertiary structure of the CFTR protein, an approach that could provide valuable insights into the structure-function relationship of CFTR. In addition, the binding site of Class I correctors (VX-809, VX-661, C18) is not well understood. Methods Exonic CFTR mutations and mutant allele frequencies described in three curated databases (ABCMdb, CFTR1 and CFTR2, comprising >130,000 data points) were mapped to two different structural models: a homology model of full-length CFTR protein in the open-channel state, and a cryo-electron microscopy core-structure of CFTR in the closed-channel state. Immunoblotting confirmed the approximate binding site of Class I correctors, and molecular docking generated binding poses for their complex with the cryo-electron microscopy structure. Results Residue positions of six high-frequency mutant CFTR alleles were found to spatially co-localize in CFTR protein, and a significant cluster was identified at the NBD1:ICL4 interdomain interface. Further, Class I correctors VX-809, VX-661 and C18 were shown to act via a similar mechanism in vitro , and a putative multi-domain corrector binding site near residues F374-L375 was predicted in silico . Conclusions Our results confirm the significance of interdomain interfaces as susceptible to disruptive mutation, and identify a putative corrector binding site. The structural pharmacogenomics approach of mapping mutation databases to protein models shows promise for facilitating drug discovery and personalized medicine for monogenetic diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".