Amyloid-Driven Allostery
Bibliographic record
Abstract
The fields of allostery and amyloid-related pathologies, such as Parkinson's disease (PD), have been extensively explored individually, but less is known about how amyloids control allostery. Recent advancements have revealed that amyloids can drive allosteric effects in both intrinsically disordered proteins, such as alpha-synuclein (αS), and multi-domain signaling proteins, such as protein kinase A (PKA). Amyloid-driven allostery plays a central role in explaining the mechanisms of gain-of-pathological-function mutations in αS ( e.g. E46K, which causes early PD onset) and loss-of-physiological-function mutations in PKA ( e.g. A211D, which predisposes to tumors). This review highlights allosteric effects of disease-related mutations and how they can cause exposure of amyloidogenic regions, leading to amyloids that are either toxic or cause aberrant signaling. We also discuss multiple potential modulators of these allosteric effects, such as MgATP and kinase substrates, opening future opportunities to improve current pharmacological interventions against αS and PKA-related pathologies. Overall, we show that amyloid-driven allosteric models are useful to explain the mechanisms underlying disease-related mutations. • Amyloid-Driven Allostery (ADA) is the amplification of allosteric effects by amyloids. • ADA is observed for both intrinsically disordered and globular multi-domain proteins. • ADA explains the mechanism of action of disease-related inherited mutations. • ADA applies to gain-of-pathological / loss-of-physiological function mutations. • Metabolites and kinase substrates modulate ADA, opening pharmacological opportunities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 |
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".