Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data.

  title={Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data.},
  author={Maxim A. Ziatdinov and Shuai Zhang and Orion Dollar and Jim Pfaendtner and Christopher J. Mundy and Xin Li and Harley Pyles and David Baker and James J. De Yoreo and Sergei V. Kalinin},
  journal={Nano letters},
The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. The deep learning (DL)-based… 
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