Identifying conformational states of macromolecules by eigen-analysis of resampled cryo-EM images.

ABSTRACT: We present the codimensional principal component analysis (PCA), a novel and straightforward method for resolving sample heterogeneity within a set of cryo-EM 2D projection images of macromolecular assemblies. The method employs PCA of resampled 3D structures computed using subsets of 2D data obtained with a novel hypergeometric sampling scheme. PCA provides us with a small subset of dominating "eigenvolumes" of the system, whose reprojections are compared with experimental projection data to yield their factorial coordinates constructed in a common framework of the 3D space of the macromolecule. Codimensional PCA is unique in the dramatic reduction of dimensionality of the problem, which facilitates rapid determination of both the plausible number of conformers in the sample and their 3D

SUBMITTER: Penczek PA 

PROVIDER: S-EPMC3255080 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature