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

Bag, Sukantadev, Prentice, Michael B., Liang, Mingzhi, Warren, Martin J. ORCID: https://orcid.org/0000-0002-6028-6456 and Roy Choudhury, Kingshuk (2016) Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions. BMC Bioinformatics, 17 (1). ISSN 1471-2105

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Abstract

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.

Item Type: Article
Additional Information: Funding Information: This work was funded by Science Foundation Ireland (SFI) Short Term Travel Fellowship 06/RFP/GEN053 STTF 08 to MBP. MBP and ML are supported by Health Research Board HRA_POR/2011/111. SB and KRC were partially supported by a Science Foundation Ireland Research Frontiers Program grant (07/REF/MA7F543) and the SFI Math Initiative. We thank Dr Alasdair W McDowall and Professor. Grant Jensen (jensenlab.caltech.edu) for assistance with obtaining cryoET data. We thank the NIH funded Duke CTSA UL1TR001117 for covering the costs of publication. Publisher Copyright: © 2016 Bag et al.
Uncontrolled Keywords: bacterial microcompartment,classification from incomplete data,cryo electron tomography,incomplete polyhedra,polyhedron graph,structural biology,biochemistry,molecular biology,computer science applications,applied mathematics ,/dk/atira/pure/subjectarea/asjc/1300/1315
Faculty \ School: Faculty of Science
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Depositing User: LivePure Connector
Date Deposited: 20 Sep 2022 14:31
Last Modified: 21 Sep 2022 00:22
URI: https://ueaeprints.uea.ac.uk/id/eprint/88488
DOI: 10.1186/s12859-016-1107-5

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