Molecular Basis for Impaired DNA Damage Response Function Associated with the RAP80 ΔE81 Defect
Bibliographic record
Abstract
Signal transduction within the DNA damage response is driven by the flux of protein-protein interaction cascades that ultimately recruit repair complexes to sites of damage. The protein RAP80 plays a central role in the damage response by targeting BRCA1/BRCA2 tumor suppressors to DNA damage foci through multivalent binding of Lys-63-linked polyubiquitin chains. Mutations within the high penetrance BRCA1/BRCA2 genes account for ∼20% of familial breast cancers. The genetic basis for the remaining cancers remains unknown, but may involve defects in binding partners for BRCA1 and BRCA2 that lead to impaired targeting to foci and a concomitant role in the pathogenesis of cancer. Recently, an in-frame deletion mutation (ΔE81) in a conserved region from the first ubiquitin interaction motif of RAP80 has been linked to an increase in chromosomal abnormalities. Using NMR spectroscopy, we demonstrate that the N-cap motif within the α-helix of the first ubiquitin interaction motif from ΔE81 undergoes a structural frameshift that leads to abolishment of multivalent binding of polyubiquitin chains. Loss of this single glutamate residue disrupts favorable electrostatic interactions between RAP80 and ubiquitin, establishing a plausible molecular basis for a potential predisposition to cancer unrelated to mutations within BRCA1/BRCA2 genes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".