Structure-Based Design of a Potent Artificial Transactivation Domain Based on p53
Bibliographic record
Abstract
Malfunctions in transcriptional regulation are associated with a number of critical human diseases. As a result, there is considerable interest in designing artificial transcription activators (ATAs) that specifically control genes linked to human diseases. Like native transcriptional activator proteins, an ATA must minimally contain a DNA-binding domain (DBD) and a transactivation domain (TAD) and, although there are several reliable methods for designing artificial DBDs, designing artificial TADs has proven difficult. In this manuscript, we present a structure-based strategy for designing short peptides containing natural amino acids that function as artificial TADs. Using a segment of the TAD of p53 as the scaffolding, modifications are introduced to increase the helical propensity of the peptides. The most active artificial TAD, termed E-Cap-(LL), is a 13-mer peptide that contains four key residues from p53, an N-capping motif and a dileucine hydrophobic bridge. In vitro analysis demonstrates that E-Cap-(LL) interacts with several known p53 target proteins, while in vivo studies in a yeast model system show that it is a 20-fold more potent transcriptional activator than the native p53-13 peptide. These results demonstrate that structure-based design represents a promising approach for developing artificial TADs that can be combined with artificial DBDs to create potent and specific ATAs.
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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) | 0.000 | 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".