A Disulfide-Stabilized Aβ that Forms Dimers but Does Not Form Fibrils
Bibliographic record
Abstract
Aβ dimers are a basic building block of many larger Aβ oligomers and are among the most neurotoxic and pathologically relevant species in Alzheimer’s disease. Homogeneous Aβ dimers are difficult to prepare, characterize, and study because Aβ forms heterogeneous mixtures of oligomers that vary in size and can rapidly aggregate into more stable fibrils. This paper introduces Aβ C18C33 as a disulfide-stabilized analogue of Aβ 42 that forms stable homogeneous dimers in lipid environments but does not aggregate to form insoluble fibrils. The Aβ C18C33 peptide is readily expressed in Escherichia coli and purified by reverse-phase HPLC to give ca. 8 mg of pure peptide per liter of bacterial culture. SDS-PAGE establishes that Aβ C18C33 forms homogeneous dimers in the membrane-like environment of SDS and that conformational stabilization of the peptide with a disulfide bond prevents the formation of heterogeneous mixtures of oligomers. Mass spectrometric (MS) studies in the presence of dodecyl maltoside (DDM) further confirm the formation of stable noncovalent dimers. Circular dichroism (CD) spectroscopy establishes that Aβ C18C33 adopts a β-sheet conformation in detergent solutions and supports a model in which the intramolecular disulfide bond induces β-hairpin folding and dimer formation in lipid environments. Thioflavin T (ThT) fluorescence assays and transmission electron microscopy (TEM) studies indicate that Aβ C18C33 does not undergo fibril formation in aqueous buffer solutions and demonstrate that the intramolecular disulfide bond prevents fibril formation. The recently published NMR structure of an Aβ 42 tetramer (PDB: 6RHY ) provides a working model for the Aβ C18C33 dimer, in which two β-hairpins assemble through hydrogen bonding to form a four-stranded antiparallel β-sheet. It is anticipated that Aβ C18C33 will serve as a stable, nonfibrilizing, and noncovalent Aβ dimer model for amyloid and Alzheimer’s disease research.
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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) | 0.001 | 0.001 |
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".