Ligand-induced disorder-to-order transitions characterized by structural proteomics and molecular dynamics simulations
Bibliographic record
Abstract
For disordered proteins, ligand binding can be a critical event that changes their structural dynamics. The ability to characterize such changes would facilitate the development of drugs designed to stabilize disordered proteins, whose mis-folding is important for a number of pathologies, including neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. In this study, we used hydrogen/deuterium exchange, differential crosslinking, differential surface modification, and molecular dynamics (MD) simulations to characterize the structural changes in disordered proteins that result from ligand binding. We show here that both an ATP-independent protein chaperone, Spy L32P, and the FK506 binding domain of a prolyl isomerase, FKBP-25 F145A/I223P, are disordered, yet exhibit structures that are distinct from chemically denatured unfolded states in solution, and that they undergo transitions to a more structured state upon ligand binding. These systems may serve as models for the characterization of ligand-induced disorder-to-order transitions in proteins using structural proteomics approaches. SIGNIFICANCE: In this study, we used hydrogen/deuterium exchange, differential crosslinking, differential surface modification, and molecular-dynamics simulations to characterize the structural changes in disordered proteins that result from ligand binding. The protein-ligand systems studied here (the ATP-independent protein chaperone, Spy L32P, and the FK506 binding domain of a prolyl isomerase, FKBP-25 F145A/I223P) may serve as models for understanding ligand-induced disorder-to-order transitions in proteins. Additionally, the structural proteomic techniques demonstrated here are shown to be effective tools for the characterization of disorder-to-order transitions and can be used to facilitate study of other systems in which this class of structural transition can be used for modulating major pathological features of disease, such as the abnormal protein aggregation that occurs with Parkinson's disease and Alzheimer's disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".