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Amyloid Fibrils

Amyloid Fibrils

Amyloid fibrils are aggregates of misfolded proteins. Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils.
Amyloid deposits were observed as early as 1639 in the liver and the spleen. In 1854, Rudolph Virchow performed iodine staining,...

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SDS-PAGE/Immunoblot Detection of Aβ Multimers in Human Cortical Tissue Homogenates using Antigen-Epitope Retrieval

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Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns.

Azam Jeihanipour¹, Jörg Lahann^1,2

¹Institute of Functional Interfaces (IFG), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.

Advanced Materials (Deerfield Beach, Fla.)

|April 11, 2022

Summary

Predicting protein mutations is challenging. Drying peptide droplets reveals structural information, enabling deep learning to identify Alzheimer's disease mutants with over 99% accuracy.

Keywords:

amyloid beta coffee ring deep learning protein misfolding self-assembly

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Published on: March 7, 2019

Area of Science:

Biochemistry
Biophysics
Computational Biology

Background:

Predicting protein mutations is a significant challenge in biochemistry.
Protein structure and mutations are linked to diseases like Alzheimer's and Parkinson's.
Understanding these alterations is crucial for disease diagnosis and treatment.

Purpose of the Study:

To develop a simple and accurate method for predicting protein mutations.
To investigate the potential of analyzing drying droplet stains for structural information.
To stratify amyloid beta (1-42) (Aβ42) variants and conformations using stain patterns.

Main Methods:

Utilized polarized light microscopy to image drying droplet deposits of Aβ42 peptides.
Employed deep-learning neural networks to analyze complex stain patterns.
Examined Aβ42 peptides with single amino acid differences, representing hereditary Alzheimer's disease mutants.

Main Results:

Stain patterns from drying peptide droplets were reproducible.
Deep learning accurately stratified eight Aβ variants with >99% predictive accuracy.
Distinct Aβ42 peptide conformations were identified with >99% accuracy.
Minute structural differences in peptide primary and secondary structures were detected.

Conclusions:

Drying droplet analysis is a simple yet effective method for inferring peptide structural information.
This technique offers a scalable and accurate approach for stratifying protein alterations.
The findings could aid in unraveling pathological signatures in neurodegenerative diseases.