Deep learning based mixed-dimensional GMM for characterizing variability in CryoEM
This method addresses the problem of analyzing protein flexibility and interactions in structural biology, offering an automated solution for researchers in cryo-electron microscopy, though it appears incremental as an improvement over existing manifold methods.
The paper tackles the challenge of characterizing continuous conformational changes and large numbers of discrete states in CryoEM data without human supervision by introducing e2gmm, a deep learning algorithm that uses a 3-D Gaussian mixture model mapped onto 2-D images, resulting in an intuitive representation that resolves structural heterogeneity and maps particles onto a small latent space.
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.