Local Structural Elements in the Mostly Unstructured Transcriptional Activation Domain of Human p53
Bibliographic record
Abstract
DNA transcription is initiated by a small regulatory region of transactivators known as the transactivation domain. In contrast to the rapid progress made on the functional aspect of this promiscuous domain, its structural feature is still poorly characterized. Here, our multidimensional NMR study reveals that an unbound full-length p53 transactivation domain, although similar to the recently discovered group of loosely folded proteins in that it does not have tertiary structure, is nevertheless populated by an amphipathic helix and two nascent turns. The helix is formed by residues Thr(18)-Leu(26) (Thr-Phe-Ser-Asp-Leu-Trp-Lys-Leu-Leu), whereas the two turns are formed by residues Met(40)-Met(44) and Asp(48)-Trp(53), respectively. It is remarkable that these local secondary structures are selectively formed by functionally critical and positionally conserved hydrophobic residues present in several acidic transactivation domains. This observation suggests that such local structures are general features of acidic transactivation domains and may represent "specificity determinants" (Ptashne, M., and Gann, A. A. F. (1997), Nature 386, 569-577) that are important for transcriptional activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".