Detection of Protein Folding Defects Caused by BRCA1-BRCT Truncation and Missense Mutations
Bibliographic record
Abstract
Most cancer-associated BRCA1 mutations identified to date result in the premature translational termination of the protein, highlighting a crucial role for the C-terminal, BRCT repeat region in mediating BRCA1 tumor suppressor function. However, the molecular and genetic effects of missense mutations that map to the BRCT region remain largely unknown. Using a protease-based assay, we directly assessed the sensitivity of the folding of the BRCT domain to an extensive set of truncation and single amino acid substitutions derived from breast cancer screening programs. The protein can tolerate truncations of up to 8 amino acids, but further deletion results in drastic BRCT folding defects. This molecular phenotype can be correlated with an increased susceptibility to disease. A cross-validated computational assessment of the BRCT mutation data base suggests that as much as half of all BRCT missense mutations contribute to BRCA1 loss of function and disease through protein-destabilizing effects. The coupled use of proteolytic methods and computational predictive methods to detect mutant BRCA1 conformations at the protein level will augment the efficacy of current BRCA1 screening protocols, especially in the absence of clinical data that can be used to discriminate deleterious BRCT missense mutations from benign polymorphisms.
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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 | 0.001 |
| 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) | 0.001 | 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".