Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions.

Published online

Journal Article

BACKGROUND: Cryo-electron tomography (cryo-ET) enables 3D imaging of macromolecular structures. Reconstructed cryo-ET images have a "missing wedge" of data loss due to limitations in rotation of the mounting stage. Most current approaches for structure determination improve cryo-ET resolution either by some form of sub-tomogram averaging or template matching, respectively precluding detection of shapes that vary across objects or are a priori unknown. Various macromolecular structures possess polyhedral structure. We propose a classification method for polyhedral shapes from incomplete individual cryo-ET reconstructions, based on topological features of an extracted polyhedral graph (PG). RESULTS: We outline a pipeline for extracting PG from 3-D cryo-ET reconstructions. For classification, we construct a reference library of regular polyhedra. Using geometric simulation, we construct a non-parametric estimate of the distribution of possible incomplete PGs. In studies with simulated data, a Bayes classifier constructed using these distributions has an average test set misclassification error of < 5 % with upto 30 % of the object missing, suggesting accurate polyhedral shape classification is possible from individual incomplete cryo-ET reconstructions. We also demonstrate how the method can be made robust to mis-specification of the PG using an SVM based classifier. The methodology is applied to cryo-ET reconstructions of 30 micro-compartments isolated from E. coli bacteria. CONCLUSIONS: The predicted shapes aren't unique, but all belong to the non-symmetric Johnson solid family, illustrating the potential of this approach to study variation in polyhedral macromolecular structures.

Full Text

Duke Authors

Cited Authors

  • Bag, S; Prentice, MB; Liang, M; Warren, MJ; Roy Choudhury, K

Published Date

  • June 13, 2016

Published In

Volume / Issue

  • 17 / 1

Start / End Page

  • 234 -

PubMed ID

  • 27296169

Pubmed Central ID

  • 27296169

Electronic International Standard Serial Number (EISSN)

  • 1471-2105

Digital Object Identifier (DOI)

  • 10.1186/s12859-016-1107-5

Language

  • eng

Conference Location

  • England