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

• Published in: BMC bioinformatics Vol. 17; no. 1; p. 234
• Main Authors: Bag, Sukantadev, Prentice, Michael B, Liang, Mingzhi, Warren, Martin J, Roy Choudhury, Kingshuk
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.06.2016; BioMed Central
• Subjects: Anisotropy; Bayes Theorem; Cryoelectron Microscopy; Electron Microscope Tomography; Escherichia coli - chemistry; Escherichia coli - ultrastructure; Image Processing, Computer-Assisted; Imaging, Three-Dimensional - methods; Methodology; Methods; Molecular structure; Observations; Tomography; Incomplete polyhedra; Polyhedron graph; Classification from incomplete data; Cryo electron tomography; Bacterial microcompartment
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-016-1107-5

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). 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. 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.