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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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Related Experiment Video

Updated: Jul 2, 2025

Rapid Generation of Amyloid from Native Proteins In vitro

Rapid Generation of Amyloid from Native Proteins In vitro

Published on: December 5, 2013

AMYGNN: A Graph Convolutional Neural Network-Based Approach for Predicting Amyloid Formation from Polypeptides.

Zuojun Yang^1,2, Yuhan Wu^1,2, Hao Liu^1,2

¹MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China.

Journal of Chemical Information and Modeling

|February 26, 2024

Summary

This study introduces AMYGNN, a novel graph convolutional neural network for predicting amyloid formation in peptide sequences. The model accurately identifies key sequences and amino acid properties essential for material construction.

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Published on: March 27, 2017

Area of Science:

Biomaterials Science
Computational Biology
Materials Chemistry

Background:

Growing interest in amyloid-based functional materials.
Need for identifying core peptide sequences for material design.
Limitations of current computational methods in analyzing polypeptide structures and amino acid interactions.

Purpose of the Study:

To develop a novel computational model for predicting amyloid formation trends in peptide sequences.
To identify crucial amino acid properties and structural features contributing to amyloid formation.
To provide a predictive framework for designing amyloid-associated functional materials.

Main Methods:

Abstracting polypeptides as graphs with amino acids as nodes.
Utilizing a graph convolutional neural network (AMYGNN).
Defining edges based on a distance threshold (Cα-Cα ≤ 5 Å) between amino acids.

Main Results:

Achieved high performance metrics: accuracy (0.9208), G-mean (0.9203), MCC (0.8417), and F1 (0.9235).
Identified 32 crucial amino acid properties contributing to amyloid formation.
Confirmed the importance of β-folding-like graph structures in polypeptides for amyloid formation.

Conclusions:

AMYGNN accurately predicts amyloid formation from peptide sequences.
The model elucidates key amino acid interactions and structural determinants.
Provides a precise framework for directing the development of novel amyloid-based functional materials.