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

ABSTRACT: Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimen

SUBMITTER: Powell BM 

PROVIDER: S-EPMC10312494 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature