Protein misfolding occurs by slow diffusion across multiple barriers in a rough energy landscape
Bibliographic record
Abstract
The timescale for the microscopic dynamics of proteins during conformational transitions is set by the intrachain diffusion coefficient, D. Despite the central role of protein misfolding and aggregation in many diseases, it has proven challenging to measure D for these processes because of their heterogeneity. We used single-molecule force spectroscopy to overcome these challenges and determine D for misfolding of the prion protein PrP. Observing directly the misfolding of individual dimers into minimal aggregates, we reconstructed the energy landscape governing nonnative structure formation. Remarkably, rather than displaying multiple pathways, as typically expected for aggregation, PrP dimers were funneled into a thermodynamically stable misfolded state along a single pathway containing several intermediates, one of which blocked native folding. Using Kramers' rate theory, D was found to be 1,000-fold slower for misfolding than for native folding, reflecting local roughening of the misfolding landscape, likely due to increased internal friction. The slow diffusion also led to much longer transit times for barrier crossing, allowing transition paths to be observed directly for the first time to our knowledge. These results open a new window onto the microscopic mechanisms governing protein misfolding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".