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Record W2055067588 · doi:10.4161/pri.23303

Single-molecule approaches to prion protein misfolding

2013· article· en· W2055067588 on OpenAlexafffund
Hao Yu, Derek R. Dee, Michael T. Woodside

Bibliographic record

VenuePrion · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersAlberta Prion Research InstituteAlberta Innovates
KeywordsPrion proteinProtein foldingFolding (DSP implementation)Computational biologyPrion ProteinsBiologyChemistryBiophysicsCell biologyDiseaseMedicine

Abstract

fetched live from OpenAlex

The structural conversion of the prion protein PrP into a transmissible, misfolded form is the central element of prion disease, yet there is little consensus as to how it occurs. Key aspects of conversion into the diseased state remain unsettled, from details about the earliest stages of misfolding such as the involvement of partially- or fully-unfolded intermediates to the structure of the infectious state. Part of the difficulty in understanding the structural conversion arises from the complexity of the underlying energy landscapes. Single molecule methods provide a powerful tool for probing complex folding pathways as in prion misfolding, because they allow rare and transient events to be observed directly. We discuss recent work applying single-molecule probes to study misfolding in prion proteins, and what it has revealed about the folding dynamics of PrP that may underlie its unique behavior. We also discuss single-molecule studies probing the interactions that stabilize non-native structures within aggregates, pointing the way to future work that may help identify the microscopic events triggering pathogenic conversion. Although single-molecule approaches to misfolding are relatively young, they have a promising future in prion science.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.228
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2013
Admission routes2
Has abstractyes

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