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Record W2109313436 · doi:10.1002/prot.21100

Structures of soluble amyloid oligomers from computer simulations

2006· article· en· W2109313436 on OpenAlexaff
Adrien S. J. Melquiond, Normand Mousseau, Philippe Derreumaux

Bibliographic record

VenueProteins Structure Function and Bioinformatics · 2006
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité de MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsFibrillogenesisFibrilMonomerBeta sheetBiophysicsAmyloid fibrilAmyloid (mycology)ChemistryMultiplicity (mathematics)CrystallographyPeptideBiochemistryBiologyPolymerAmyloid β

Abstract

fetched live from OpenAlex

Alzheimer's, Parkinson's, and Creutzfeldt-Jakob's neurodegenerative diseases are all linked with the assembly of normally soluble proteins into amyloid fibrils. Because of experimental limitations, structural characterization of the soluble oligomers, which form early in the process of fibrillogenesis and are cytotoxic, remains to be determined. In this article, we study the aggregation paths of seven chains of the shortest amyloid-forming peptide, using an activitated method and a reduced atomic representation. Our simulations show that disordered KFFE monomers ultimately form three distinct topologies of similar energy: amorphous oligomers, incomplete rings with beta-barrel character, and cross-beta-sheet structures with the meridional but not the equatorial X-ray fiber reflections. The simulations also shed light on the pathways from misfolded aggregates to fibrillar-like structures. They also underline the multiplicity of building blocks that can lead to the formation of the critical nucleus from which rapid growth of the fibril occurs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations61
Published2006
Admission routes1
Has abstractyes

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