  Structural flexibility and/or dynamic interactions with other molecules is a
critical aspect of protein function. CryoEM provides direct visualization of
individual macromolecules sampling different conformational and compositional
states. While numerous methods are available for computational classification
of discrete states, characterization of continuous conformational changes or
large numbers of discrete state without human supervision remains challenging.
Here we present e2gmm, 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, e2gmm 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 both simulated data as well as
three biological systems, to explore compositional and conformational changes
at a range of scales. The software is distributed as part of EMAN2.
