Bayesian analysis of individual electron microscopy images: towards structures of dynamic and heterogeneous biomolecular assemblies.

ABSTRACT: We develop a method to extract structural information from electron microscopy (EM) images of dynamic and heterogeneous molecular assemblies. To overcome the challenge of disorder in the imaged structures, we analyze each image individually, avoiding information loss through clustering or averaging. The Bayesian inference of EM (BioEM) method uses a likelihood-based probabilistic measure to quantify the consistency between each EM image and given structural models. The likelihood function accounts for uncertainties in the molecular position and orientation, variations in the relative intensities and noise in the experimental images. The BioEM formalism is physically intuitive and mathematically simple. We show that for experimental GroEL images, BioEM correctly identifies structures accord

SUBMITTER: Cossio P 

PROVIDER: S-EPMC3855270 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature