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Inhibition of Alzheimer's Amyloidosis by Peptides That Prevent β-Sheet Conformation

Open access
Soto, Claudio, Kindy, Mark S., Baumann, Marc, Frangione, Blas·1996·Biochemical and Biophysical Research Communications

Abstract

Amyloid beta-peptide (A beta) is a major fibrillar component of neuritic plaques in Alzheimer's disease (AD) brains and is related to the pathogenesis of the disease. We hypothesized that amyloid formation could be inhibited by peptides homologous to A beta (position 17-21) with a similar degree of hydrophobicity, but with a very low propensity to adopt a beta-sheet conformation by incorporating proline residues (anti-beta-sheet peptides or beta-sheet inhibitors). An 11-residue peptide with these characteristics binds to A beta, inhibits A beta fibril formation and partially disaggregates preformed fibrils in vitro. Shorter anti-beta-sheet peptides and analogs containing D-amino acids are also able to inhibit A beta fibrillogenesis. The latter are more resistant to proteolytic degradation and may serve as a starting point to design more efficient peptides derivatives to inhibit amyloidogenesis in vivo.

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Metadata

Authors
Soto, C., Kindy, M. S., Baumann, M., & Frangione, B.
Venue
Biochemical and Biophysical Research Communications
Publisher
Elsevier BV
Published
1996-09
Volume
226
Issue
3
Pages
672-680
ISSN
0006-291X (ISSN-L)
OpenAlex
W2092662963
Cited by
431
References
0
FWCI
6.91
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Inhibition of Alzheimer's Amyloidosis by Peptides That Prevent β-Sheet Conformation. https://app.cochat.ai/papers/10.1006/bbrc.1996.1413
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Soto, C., Kindy, M. S., Baumann, M., & Frangione, B. (1996). Inhibition of Alzheimer's Amyloidosis by Peptides That Prevent β-Sheet Conformation. Biochemical and Biophysical Research Communications. https://doi.org/10.1006/bbrc.1996.1413

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.