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.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".