Effect of amino‐acid substitutions on Alzheimer's amyloid‐β peptide–glycosaminoglycan interactions
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Bibliographic record
Abstract
One of the major clinical features of Alzheimer's disease is the presence of extracellular amyloid plaques that are associated with glycosaminoglycan-containing proteoglycans. It has been proposed that proteoglycans and glycosaminoglycans facilitate amyloid fibril formation and/or stabilize these aggregates. Characterization of proteoglycan-protein interactions has suggested that basic amino acids in a specific conformation are necessary for glycosaminoglycan binding. Amyloid-beta peptide (Abeta) has a cluster of basic amino acids at the N-terminus (residues 13-16, His-His-Gln-Lys), which are considered critical for glycosaminoglycan interactions. To understand the molecular recognition of glycosaminoglycans by Abeta, we have examined a series of synthetic peptides with systematic alanine substitutions. These include: His13-->Ala, His14-->Ala, Lys16-->Ala, His13His14Lys16-->Ala and Arg5His6-->Ala. Alanine substitutions result in differences in both the secondary and fibrous structure of Abeta1-28 as determined by circular dichroism spectroscopy and electron microscopy. The results demonstrate that the His-His-Gln-Lys region of Abeta, and in particular His13, is an important structural domain, as Ala substitution produces a dysfunctional folding mutant. Interaction of the substituted peptides with heparin and chondroitin sulfate glycosaminoglycans demonstrate that although electrostatic interactions contribute to binding, nonionic interactions such as hydrogen bonding and van der Waals packing play a role in glycosaminoglycan-induced Abeta folding and aggregation.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it