Marc Aurèle Gilles

The paper presents a cryo-EM conformational heterogeneity challenge to evaluate and improve methods for analyzing molecular motions in heterogeneous samples.

The paper warns that counting particle images in cryo-EM can lead to biased estimates of conformational state populations due to noise and orientation uncertainty, and suggests using full image datasets for more accurate modeling.

The paper introduces RECOVAR, a method for analyzing cryo-EM data heterogeneity using regularized covariance estimation and kernel regression, achieving high-resolution conformational state reconstruction and interpretable results.