Searching for Factors that Distinguish Disease-Prone and Disease-Resistant Prions via Sequence Analysis
Bibliographic record
Abstract
The exact mechanisms of prion misfolding and factors that predispose an individual to prion diseases are largely unknown. Our approach to identifying candidate factors in-silico relies on contrasting the C-terminal domain of PrP(C) sequences from two groups of vertebrate species: those that have been found to suffer from prion diseases, and those that have not. We propose that any significant differences between the two groups are candidate factors that may predispose individuals to develop prion disease, which should be further analyzed by wet-lab investigations. Using an array of computational methods we identified possible point mutations that could predispose PrP(C) to misfold into PrP(Sc). Our results include confirmatory findings such as the V210I mutation, and new findings including P137M, G142D, G142N, D144P, K185T, V189I, H187Y and T191P mutations, which could impact structural stability. We also propose new hypotheses that give insights into the stability of helix-2 and -3. These include destabilizing effects of Histidine and T188-T193 segment in helix-2 in the disease-prone prions, and a stabilizing effect of Leucine on helix-3 in the disease-resistant prions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".