Effect of amino‐acid substitutions on Alzheimer's amyloid‐β peptide–glycosaminoglycan interactions
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".