AM by Jörg Lahann's team at the Karlsruhe Institute of Technology in Germany: Deep learning-assisted stratification of β amyloid mutants using a dried droplet pattern

Proteins have complex biochemical structures, and a large number of studies and experimental evidence in recent years show that even small structural or conformational changes can affect the biological function of proteins and lead to the occurrence of disease mechanisms

Recently, Jörg Lahann's team from the Karlsruhe Institute of Technology in Germany published a research paper on Advanced Materials, using a pre-trained deep learning algorithm to analyze the structural characteristics of pathogenic β amyloid according to the sedimentary pattern left by dried droplets, achieving very high prediction accuracy

In this work, the researchers used PLM microscopy to analyze the sedimentation patterns of peptide-containing droplets dried on a hydrophobic substrate, showing a simple but structure-rich supramolecular system that is controlled

After that, the authors compared the accuracy and training burden of 16 deep learning algorithms, and finally selected the small and medium-sized deep learning network NasNet-Large for follow-up experiments

To identify the characteristics of the discriminant region of the PLM image, the authors used a gradient-weighted class activation map (Grad-CAM) to generate an activation graph

WILEY

Thesis Information:

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

Azam Jeihanipour, Jörg Lahann

Advanced Materials

DOI：10.

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