Nanopore Analysis of Wild-Type and Mutant Prion Protein (PrPC): Single Molecule Discrimination and PrPC Kinetics
Bibliographic record
Abstract
Prion diseases are fatal neurodegenerative diseases associated with the conversion of cellular prion protein (PrP(C)) in the central nervous system into the infectious isoform (PrP(Sc)). The mechanics of conversion are almost entirely unknown, with understanding stymied by the lack of an atomic-level structure for PrP(Sc). A number of pathogenic PrP(C) mutants exist that are characterized by an increased propensity for conversion into PrP(Sc) and that differ from wild-type by only a single amino-acid point mutation in their primary structure. These mutations are known to perturb the stability and conformational dynamics of the protein. Understanding of how this occurs may provide insight into the mechanism of PrP(C) conversion. In this work we sought to explore wild-type and pathogenic mutant prion protein structure and dynamics by analysis of the current fluctuations through an organic α-hemolysin nanometer-scale pore (nanopore) in which a single prion protein has been captured electrophoretically. In doing this, we find that wild-type and D178N mutant PrP(C), (a PrP(C) mutant associated with both Fatal Familial Insomnia and Creutzfeldt-Jakob disease), exhibit easily distinguishable current signatures and kinetics inside the pore and we further demonstrate, with the use of Hidden Markov Model signal processing, accurate discrimination between these two proteins at the single molecule level based on the kinetics of a single PrP(C) capture event. Moreover, we present a four-state model to describe wild-type PrP(C) kinetics in the pore as a first step in our investigation on characterizing the differences in kinetics and conformational dynamics between wild-type and D178N mutant PrP(C). These results demonstrate the potential of nanopore analysis for highly sensitive, real-time protein and small molecule detection based on single molecule kinetics inside a nanopore, and show the utility of this technique as an assay to probe differences in stability between wild-type and mutant prion proteins at the single molecule level.
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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 | 0.000 | 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) | 0.000 | 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".