Hierarchically ordered multi-timescale structural dynamics of the intrinsically disordered p53 transactivation domain
Bibliographic record
Abstract
ABSTRACT Intrinsically disordered proteins (IDPs) exhibit pronounced structural dynamics, which is crucial for their functional versatility. Yet their dynamics slower than nanoseconds remain largely elusive. We combined high-power relaxation dispersion nuclear magnetic resonance spectroscopy with molecular dynamics simulations to characterize these kinetics and the underlying structural interconversions of a prototypical IDP, the N-terminal transactivation domain of the tumor suppressor p53 (p53-TAD). We find a complex hierarchy of structural dynamics on timescales covering over seven orders of magnitude, ranging from fast nanoseconds backbone re-orientations, via sub-microsecond helix-formation dynamics involving many structural sub-states and transition times, to transient tertiary structure formation slower than 25 microseconds. These rich structural dynamics of p53-TAD, and likely those of other IDPs, parallel the timescale hierarchy of the conformational dynamics of folded proteins. One-Sentence summary A hierarchical energy landscape governs kinetics and structural dynamics of the disordered p53 transactivation domain.
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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".