Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs

Abstract

Cryo-electron microscopy (cryo-EM) has revolutionized experimental protein structure determination. Despite advances in high resolution reconstruction, a majority of cryo-EM experiments provide either a single state of the studied macromolecule, or a relatively small number of its conformations. This reduces the effectiveness of the technique for proteins with flexible regions, which are known to play a key role in protein function. Recent methods for capturing conformational heterogeneity in cryo-EM data model it in volume space, making recovery of continuous atomic structures challenging. Here we present a fully deep-learning-based approach using variational auto-encoders (VAEs) to recover a continuous distribution of atomic protein structures and poses directly from picked particle images and demonstrate its efficacy on realistic simulated data. We hope that methods built on this work will allow incorporation of stronger prior information about protein structure and enable better understanding of non-rigid protein structures.

References in corpus (1)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Cited by in corpus (5)

• Exploring generative atomic models in cryo-EM reconstruction
• An approximate expectation-maximization for two-dimensional multi-target detection
• Spectral decomposition of atomic structures in heterogeneous cryo-EM
• Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters
• End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data