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Record W1992979903 · doi:10.1038/srep02781

α-Helical Structures Drive Early Stages of Self-Assembly of Amyloidogenic Amyloid Polypeptide Aggregate Formation in Membranes

2013· article· en· W1992979903 on OpenAlexafffund
Martina Pannuzzo, Antonio Raudino, Danilo Milardi, Carmelo La Rosa, Mikko Karttunen

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAmyloid (mycology)IsletMembraneBiophysicsChemistryAmylinPeptideAmyloid fibrilBiochemistryAmyloid βBiologyInsulin

Abstract

fetched live from OpenAlex

The human islet amyloid polypeptide (hIAPP) is the primary component in the toxic islet amyloid deposits in type-2 diabetes. hIAPP self-assembles to aggregates that permeabilize membranes and constitutes amyloid plaques. Uncovering the mechanisms of amyloid self-assembly is the key to understanding amyloid toxicity and treatment. Although structurally similar, hIAPP's rat counterpart, the rat islet amyloid polypeptide (rIAPP), is non-toxic. It has been a puzzle why these peptides behave so differently. We combined multiscale modelling and theory to explain the drastically different dynamics of hIAPP and rIAPP: The differences stem from electrostatic dipolar interactions. hIAPP forms pentameric aggregates with the hydrophobic residues facing the membrane core and stabilizing water-conducting pores. We give predictions for pore sizes, the number of hIAPP peptides, and aggregate morphology. We show the importance of curvature-induced stress at the early stages of hIAPP assembly and the α-helical structures over β-sheets. This agrees with recent fluorescence spectroscopy experiments.

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 score0.152
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.276
Teacher spread0.262 · 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

Citations105
Published2013
Admission routes2
Has abstractyes

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