Designed Ankyrin Repeat Proteins as a tool box for analyzing p63
Bibliographic record
Abstract
The function of the p53 transcription factor family is dependent on several folded domains. In addition to a DNA-binding domain, members of this family contain an oligomerization domain. p63 and p73 also contain a C-terminal Sterile α-motif domain. Inhibition of most transcription factors is difficult as most of them lack deep pockets that can be targeted by small organic molecules. Genetic knock-out procedures are powerful in identifying the overall function of a protein, but they do not easily allow one to investigate roles of individual domains. Here we describe the characterization of Designed Ankyrin Repeat Proteins (DARPins) that were selected as tight binders against all folded domains of p63. We determine binding affinities as well as specificities within the p53 protein family and show that DARPins can be used as intracellular inhibitors for the modulation of transcriptional activity. By selectively inhibiting DNA binding of the ΔNp63α isoform that competes with p53 for the same promoter sites, we show that p53 can be reactivated. We further show that inhibiting the DNA binding activity stabilizes p63, thus providing evidence for a transcriptionally regulated negative feedback loop. Furthermore, the ability of DARPins to bind to the DNA-binding domain and the Sterile α-motif domain within the dimeric-only and DNA-binding incompetent conformation of TAp63α suggests a high structural plasticity within this special conformation. In addition, the developed DARPins can also be used to specifically detect p63 in cell culture and in primary tissue and thus constitute a very versatile research tool for studying the function of p63.
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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) | 0.001 | 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".