Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions.
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.
Statistics Department, University College Cork, Cork, Ireland.
Sukantadev
S
Bag
Department of Microbiology, University College Cork, Cork, Ireland.
Michael B
MB
Prentice
School of Biosciences, University of Kent, Canterbury, UK.
Mingzhi
M
Liang
School of Biosciences, University of Kent, Canterbury, UK.
Martin J
MJ
Warren
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, 27705, USA. kingshuk@duke.edu.
Kingshuk
K
Roy Choudhury
D016880:Anisotropy; D001499:Bayes Theorem; D020285:Cryoelectron Microscopy; D055032:Electron Microscope Tomography; D004926:Escherichia coli / Q000737:chemistry* / Q000648:ultrastructure; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods
Bacterial microcompartment; Classification from incomplete data; Cryo electron tomography; Incomplete polyhedra; Polyhedron graph