Deep learning based mixed-dimensional GMM for characterizing variability in CryoEM

TL;DR

This paper introduces e2gmm, a deep learning algorithm that characterizes continuous conformational variability in proteins from CryoEM data by mapping 3D Gaussian mixture models onto 2D images, enabling detailed structural heterogeneity analysis.

Contribution

The paper presents a novel deep neural network-based method, e2gmm, for automatically resolving structural heterogeneity and mapping conformational landscapes directly from CryoEM images.

Findings

Successfully applied to simulated data and biological systems

Effectively resolves conformational and compositional heterogeneity

Provides a flexible, intuitive representation of structural variability

Abstract

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.