Deep learning based mixed-dimensional GMM for characterizing variability in CryoEM. (arXiv:2101.10356v1 [q-bio.BM])

The function of most protein molecules involves structural flexibility and/or
dynamic interactions with other molecules. CryoEM provides direct visualization
of individual macromolecules in different conformational and compositional
states. While many methods are available for classification of discrete states,
characterization of continuous conformational changes or large numbers of
discrete state without human supervision remains challenging. Here we present a
machine learning algorithm to determine a conformational landscape for proteins
or complexes using a 3-D Gaussian mixture model mapped onto 2-D particle images
in known orientations. Using a deep neural network architecture, this method
can automatically resolve the structural heterogeneity within the protein
complex and map particles onto a small latent space describing conformational
and compositional changes. This system presents a more intuitive and flexible
representation than other manifold methods currently in use. We demonstrate
this method on several different biomolecular systems to explore compositional
and conformational changes at a range of scales.



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