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Record W2035891349 · doi:10.1021/ja047286i

In Silico Assembly of Alzheimer's Aβ<sub>16</sub><sub>-</sub><sub>22</sub> Peptide into β-Sheets

2004· article· en· W2035891349 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversité de MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsAntiparallel (mathematics)ChemistryFibrilMonomerPeptideFolding (DSP implementation)CrystallographyBeta sheetBETA (programming language)In silicoAmyloid betaBiophysicsProtein foldingMolecular dynamicsRelaxation (psychology)Computational chemistryBiochemistry

Abstract

fetched live from OpenAlex

Recent studies suggest that soluble oligomers of amyloid-forming peptides have toxic effects in cell cultures. In this study, the folding of three Alzheimer's A beta(16-22) peptides have been simulated with the activation-relaxation technique and a generic energy model. Starting from randomly chosen states, the predicted lowest energy structure superposes within 1 A rms deviation from its conformation within the fibrils. This antiparallel structure is found to be in equilibrium with several out-of-register antiparallel beta-sheets and mixed parallel-antiparallel beta-sheets, indicating that full structural order in the fibrils requires larger aggregates. Folding involves the formation of dimers followed by the addition of a monomer and proceeds through a generalized mechanism between disordered and native alignments of beta-strands.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.256
Teacher spread0.246 · 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