Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations
Source abstract
Abstract
The dynamics of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for lateral shifts. The disentangled representations in the latent space encode the rich spectrum of local transitions that can now be visualized and explored via continuous variables. The time dependence of ensemble averages allows insight into the time dynamics of the system and, in particular, illustrates the presence of the potential ordering transition. Finally, analysis of the latent variables along the single-particle trajectory allows tracing these parameters on a single-particle level. The proposed approach is expected to be universally applicable for the description of the imaging data in optical, scanning probe, and electron microscopy seeking to understand the dynamics of complex systems where rotations are a significant part of the process.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Mass Spectrometry Techniques and Applications · Molecular spectroscopy and chirality · Advanced Proteomics Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, Tennessee 37996, United States
Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
Institute for Protein Design, University of Washington, Seattle, Washington 98195, United States
Howard Hughes Medical Institute, University of Washington, Seattle, Washington 98195, United States
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
Funding
Basic Energy Sciences · U.S. Department of Energy
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