Energy landscape analysis of native folding of the prion protein yields the diffusion constant, transition path time, and rates
Bibliographic record
Abstract
Protein folding is described conceptually in terms of diffusion over a configurational free-energy landscape, typically reduced to a one-dimensional profile along a reaction coordinate. In principle, kinetic properties can be predicted directly from the landscape profile using Kramers theory for diffusive barrier crossing, including the folding rates and the transition time for crossing the barrier. Landscape theory has been widely applied to interpret the time scales for protein conformational dynamics, but protein folding rates and transition times have not been calculated directly from experimentally measured free-energy profiles. We characterized the energy landscape for native folding of the prion protein using force spectroscopy, measuring the change in extension of a single protein molecule at high resolution as it unfolded/refolded under tension. Key parameters describing the landscape profile were first recovered from the distributions of unfolding and refolding forces, allowing the diffusion constant for barrier crossing and the transition path time across the barrier to be calculated. The full landscape profile was then reconstructed from force-extension curves, revealing a double-well potential with an extended, partially unfolded transition state. The barrier height and position were consistent with the previous results. Finally, Kramers theory was used to predict the folding rates from the landscape profile, recovering the values observed experimentally both under tension and at zero force in ensemble experiments. These results demonstrate how advances in single-molecule theory and experiment are harnessing the power of landscape formalisms to describe quantitatively the mechanics of folding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".