Structural Basis for ASPP2 Recognition by the Tumor Suppressor p73
Bibliographic record
Abstract
Tumor suppressors p53, p63 and p73 comprise a family of stress-responsive transcription factors with distinct functions in development and tumor suppression. Most human cancers lose p53 function, yet all three proteins are capable of inducing apoptosis or cellular senescence. Mechanisms are therefore under investigation to activate p73-dependent apoptosis in p53-deficient cancer cells. Significantly, the DNA-binding domain (DBD) of p73 escapes viral oncoproteins and displays an enhanced thermal stability. To further understand the variant features of p73, we solved the high-resolution crystal structure of the p73 DBD as well as its complex with the ankyrin repeat and SH3 domains of the pro-apoptotic factor ASPP2. The p73 structure exhibits the same conserved architecture as p53 but displays a divergent L2 loop, a known site of protein-protein interaction. The loop in p73 is changed by a two-residue insertion that also induces repacking around the site of the p53 mutational hotspot R175. Importantly, the binding of ASPP2 is preserved by conformational changes in both the ankyrin repeat and SH3 domains. These results further highlight the structural variation that impacts p53 family interactions within the p53 interactome.
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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 | 0.001 |
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".