Folding or holding?—Hsp70 and Hsp90 chaperoning of misfolded proteins in neurodegenerative disease
Bibliographic record
Abstract
The toxic accumulation of misfolded proteins as inclusions, fibrils, or aggregates is a hallmark of many neurodegenerative diseases. However, how molecular chaperones, such as heat shock protein 70 kDa (Hsp70) and heat shock protein 90 kDa (Hsp90), defend cells against the accumulation of misfolded proteins remains unclear. The ATP-dependent foldase function of both Hsp70 and Hsp90 actively transitions misfolded proteins back to their native conformation. By contrast, the ATP-independent holdase function of Hsp70 and Hsp90 prevents the accumulation of misfolded proteins. Foldase and holdase functions can protect against the toxicity associated with protein misfolding, yet we are only beginning to understand the mechanisms through which they modulate neurodegeneration. This review compares recent structural findings regarding the binding of Hsp90 to misfolded and intrinsically disordered proteins, such as tau, α-synuclein, and Tar DNA-binding protein 43. We propose that Hsp90 and Hsp70 interact with these proteins through an extended and dynamic interface that spans the surface of multiple domains of the chaperone proteins. This contrasts with many other Hsp90-client protein interactions for which only a single bound conformation of Hsp90 is proposed. The dynamic nature of these multidomain interactions allows for polymorphic binding of multiple conformations to vast regions of Hsp90. The holdase functions of Hsp70 and Hsp90 may thus allow neuronal cells to modulate misfolded proteins more efficiently by reducing the long-term ATP running costs of the chaperone budget. However, it remains unclear whether holdase functions protect cells by preventing aggregate formation or can increase neurotoxicity by inadvertently stabilizing deleterious oligomers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".