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

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 workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

Automated summary

High-speed atomic force microscopy is used to visualize the dynamics of protein self-assembly on inorganic surfaces and the resulting geometric patterns. Unsupervised linear unmixing is utilized to explore the time dynamics of classical macroscopic descriptors and establish the presence of static ordered and dynamic disordered phases. Deep learning-based workflow is developed to analyze particle dynamics and identify particle behavior classes through feature extraction and mixture modeling.