Cryo-EM heterogeneity analysis using regularized covariance estimation and kernel regression.

ABSTRACT: Proteins and the complexes they form are central to nearly all cellular processes. Their flexibility, expressed through a continuum of states, provides a window into their biological functions. Cryogenic electron microscopy (cryo-EM) is an ideal tool to study these dynamic states as it captures specimens in noncrystalline conditions and enables high-resolution reconstructions. However, analyzing the heterogeneous distributions of conformations from cryo-EM data is challenging. We present RECOVAR, a method for analyzing these distributions based on principal component analysis (PCA) computed using a REgularized COVARiance estimator. RECOVAR is fast, robust, interpretable, expressive, and competitive with state-of-the-art neural network methods on heterogeneous cryo-EM datasets. The regulari

SUBMITTER: Gilles MA 

PROVIDER: S-EPMC11892586 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature