Covariance estimation using conjugate gradient for 3D classification in Cryo-EM
This work addresses the challenge of 3D classification in Cryo-EM for structural biologists, but it is incremental as it builds on a previous method.
The authors tackled the problem of classifying structural variability in noisy Cryo-EM projections by proposing a method for covariance estimation that incorporates contrast transfer function and non-uniform viewing angles, resulting in improved performance on synthetic and experimental datasets, including a 70S ribosome complex.
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.