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Intrinsically Disordered Proteins

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Intrinsically disordered proteins are a group of proteins that do not fold into specific three-dimensional structures. Their structural flexibility allows them to complement ordered proteins to perform functions that are inaccessible to rigid structures. They are more common in eukaryotes than prokaryotes and may either be exclusively intrinsically disordered or hybrid proteins, consisting of a mix of ordered and disordered regions. The absence of a rigid structure in these proteins can be...

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Related Experiment Video

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Machine learning identifies scale-free properties in disordered materials.

Sunkyu Yu^1,2, Xianji Piao¹, Namkyoo Park³

¹Photonic Systems Laboratory, Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Korea.

Nature Communications

|September 25, 2020

Summary

This summary is machine-generated.

Machine learning predicts wave localization in disordered systems. This approach identifies scale-free structures, significantly improving robustness to defects in wave-matter interactions.

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Area of Science:

Physics
Materials Science
Machine Learning

Background:

Disordered systems offer vast design freedom for signal processing.
Deterministic design of these systems for specific functionalities remains challenging.

Purpose of the Study:

To develop a machine learning approach for predicting and designing wave-matter interactions in disordered structures.
To identify scale-free properties in disordered systems for enhanced wave behavior.

Main Methods:

Developed disorder-to-localization and localization-to-disorder convolutional neural networks (CNNs).
CNNs enable instantaneous prediction of wave localization and generation of disordered structures.
Analyzed network architectures to identify structural properties linked to wave behavior.

Main Results:

Identified scale-free disordered structures with heavy-tailed distributions.
Achieved significant improvements in robustness to accidental defects (multiple orders of magnitude).
Demonstrated the critical role of neural network architecture in structure generation and defect immunity.

Conclusions:

Machine learning, specifically CNNs, can effectively predict and design wave-matter interactions in disordered systems.
Scale-free properties are key to creating robust disordered structures.
Neural network design directly influences the properties and defect immunity of generated physical structures.