Quantifying the dynamics of protein self-organization using deep learning analysis of atomic force microscopy data

TL;DR

This paper introduces a deep learning workflow to analyze atomic force microscopy data, revealing detailed protein self-organization dynamics, local geometries, and interaction potentials, advancing understanding of complex system behaviors.

Contribution

The study develops a novel deep learning-based method for analyzing protein self-assembly dynamics at the particle level from microscopy data, including interaction potential reconstruction and behavior classification.

Findings

Identified static and dynamic phases in protein self-organization.

Reconstructed interaction potentials between particles.

Classified particle behaviors and transition probabilities.

Abstract

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 (FFT), correlation and pair distribution function 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 on the particle-by-particle level. Beyond the macroscopic descriptors, we utilize the knowledge of local particle geometries and configurations to explore the evolution of local geometries and reconstruct the interaction potential between the particles. Finally, we use the machine learning-based feature extraction to define particle neighborhood free of physics constraints. This approach allowed separating the possible classes of particle behavior, identify the associated transition probabilities, and further extend this analysis to identify slow modes and associated configurations, allowing for systematic exploration and predictive modeling of the time dynamics of the system. Overall, this work establishes the DL based workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.