Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

ABSTRACT: Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDR

SUBMITTER: Powell BM 

PROVIDER: S-EPMC11655136 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature