Enhanced Binding Affinity via Destabilization of the Unbound State: A Millisecond Hydrogen–Deuterium Exchange Study of the Interaction between p53 and a Pleckstrin Homology Domain
Bibliographic record
Abstract
The incorporation of intrinsically disordered domains enables proteins to engage a wide variety of targets, with phosphorylation often modulating target specificity and affinity. Although phosphorylation can clearly act as a chemical driver of complexation in structured proteins, e.g., by abrogating or permitting new charge-charge interactions, the basis for enhancement of the hydrophobically driven interactions that are typical of disordered protein-target complexation is less clear. To determine how phosphorylation can positively impact target recruitment in disordered domains, we have examined the interaction between the disordered N-terminal transactivation domain (TAD) of p53 and the pleckstrin homology (PH) domain of p62. Using time-resolved electrospray ionization with hydrogen-deuterium exchange, we demonstrate that phosphorylation has little effect on the conformation of the p53 TAD when it is bound to the PH domain but instead increases the degree of conformational disorder in the unbound state. We propose that this increase in the degree of disorder creates a wider free energy gap between the free and bound states, providing a target-independent mechanism for enhanced binding when the phosphorylated and unphosphorylated p53-target complexes have similar free energies.
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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 | 0.001 |
| 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".