DARPins detect the formation of hetero-tetramers of p63 and p73 in epithelial tissues and in squamous cell carcinoma
Bibliographic record
Abstract
Abstract The two p53 homologues p63 and p73 regulate transcriptional programs in epithelial tissues and several cell types in these tissues express both proteins. All members of the p53 family form tetramers in their active state through a dedicated oligomerization domain that structurally assembles as a dimer of dimers. The oligomerization domain of p63 and p73 share a high sequence identity, but the p53 oligomerization domain is more divergent and it lacks a functionally important C-terminal helix present in the other two family members. Based on these structural differences, p53 does not hetero-oligomerize with p63 or p73. In contrast, p63 and p73 form hetero-oligomers of all possible stoichiometries, with the hetero-tetramer built from a p63 dimer and a p73 dimer being thermodynamically more stable than the two homo-tetramers. This predicts that in cells expressing both proteins a p632/p732 hetero-tetramer is formed. So far, the tools to investigate the biological function of this hetero-tetramer have been missing. Here we report the generation and characterization of Designed Ankyrin Repeat Proteins (DARPins) that bind with high affinity and selectivity to the p632/p732 hetero-tetramer. Using these DARPins we were able to confirm experimentally the existence of this hetero-tetramer in epithelial mouse and human tissues and show that its level increases in squamous cell carcinoma.
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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 | 0.000 |
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".