Covariance estimation using conjugate gradient for 3D classification in Cryo-EM

Abstract

Classifying structural variability in noisy projections of biological macromolecules is a central problem in Cryo-EM. In this work, we build on a previous method for estimating the covariance matrix of the three-dimensional structure present in the molecules being imaged. Our proposed method allows for incorporation of contrast transfer function and non-uniform distribution of viewing angles, making it more suitable for real-world data. We evaluate its performance on a synthetic dataset and an experimental dataset obtained by imaging a 70S ribosome complex.

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• Hyper-Molecules: on the Representation and Recovery of Dynamical Structures, with Application to Flexible Macro-Molecular Structures in Cryo-EM
• Continuously heterogeneous hyper-objects in cryo-EM and 3-D movies of many temporal dimensions
• Spectral decomposition of atomic structures in heterogeneous cryo-EM
• Reconstruction of stochastic 3-D signals with symmetric statistics from 2-D projection images motivated by cryo-electron microscopy
• Computational Methods for Single-Particle Cryo-EM