Stereoselective Interactions of Peptide Inhibitors with the β-Amyloid Peptide
Bibliographic record
Abstract
Residues 16-20 of the beta-amyloid peptide (A beta) function as a self-recognition element during A beta assembly into fibers. Peptides containing this motif retain the ability to interact with A beta and, in some cases, potently inhibit its assembly. Replacing L- with D-amino acids could stabilize such peptides and permit their evaluation as therapeutic agents for Alzheimer's disease. Here we have assessed the effect that such a chiral reversal has on inhibitory potency. D-enantiomers of five peptides, KLVFFA, KKLVFFA, KFVFFA, KIVFFA, and KVVFFA, were unexpectedly more active as inhibitors in an in vitro fibrillogenesis assay. Circular dichroism showed that D-KLVFFA more effectively prevented A beta adopting the beta-sheet secondary structure correlated with fibrillogenesis. Electron microscopy showed that fiber formation was also more strongly inhibited by D-KLVFFA. Heterochiral inhibition was confirmed using D-A beta, on the principle that enantiomeric proteins exhibit reciprocal chiral biochemical interactions. With D-Abeta, L-KLVFFA was the more potent inhibitor, rather than d-KLVFFA. Most significantly, D-peptides were more potent at reducing the toxicity of both A beta1-40 and A beta 1-42 toward neuronal cells in culture. This unforeseen heterochiral stereoselectivity of A beta for D-peptide inhibitors should be considered during future design of peptide-based inhibitors of A beta neurotoxicity and fibrillogenesis.
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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 | 0.000 |
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".