Prion disease susceptibility is affected by β-structure folding propensity and local side-chain interactions in PrP
Bibliographic record
Abstract
Prion diseases occur when the normally α-helical prion protein (PrP) converts to a pathological β-structured state with prion infectivity (PrP(Sc)). Exposure to PrP(Sc) from other mammals can catalyze this conversion. Evidence from experimental and accidental transmission of prions suggests that mammals vary in their prion disease susceptibility: Hamsters and mice show relatively high susceptibility, whereas rabbits, horses, and dogs show low susceptibility. Using a novel approach to quantify conformational states of PrP by circular dichroism (CD), we find that prion susceptibility tracks with the intrinsic propensity of mammalian PrP to convert from the native, α-helical state to a cytotoxic β-structured state, which exists in a monomer-octamer equilibrium. It has been controversial whether β-structured monomers exist at acidic pH; sedimentation equilibrium and dual-wavelength CD evidence is presented for an equilibrium between a β-structured monomer and octamer in some acidic pH conditions. Our X-ray crystallographic structure of rabbit PrP has identified a key helix-capping motif implicated in the low prion disease susceptibility of rabbits. Removal of this capping motif increases the β-structure folding propensity of rabbit PrP to match that of PrP from mouse, a species more susceptible to prion disease.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".