Deep learning-based subdivision approach for large scale macromolecules structure recovery from electron cryo tomograms.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 15, 2017

Abstract

MOTIVATION: Cellular Electron CryoTomography (CECT) enables 3D visualization of cellular organization at near-native state and in sub-molecular resolution, making it a powerful tool for analyzing structures of macromolecular complexes and their spatial organizations inside single cells. However, high degree of structural complexity together with practical imaging limitations makes the systematic de novo discovery of structures within cells challenging. It would likely require averaging and classifying millions of subtomograms potentially containing hundreds of highly heterogeneous structural classes. Although it is no longer difficult to acquire CECT data containing such amount of subtomograms due to advances in data acquisition automation, existing computational approaches have very limited scalability or discrimination ability, making them incapable of processing such amount of data.

Authors

  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Xiaoqi Chai
    Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Hariank Muthakana
    Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Xiaodan Liang
    Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Ge Yang
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, Hubei Province, 430070 China.
  • Tzviya Zeev-Ben-Mordehai
    Division of Structural Biology, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK; Cryo-electron Microscopy, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands.
  • Eric P Xing
    Department of Machine Learning, Carnegie-Mellon University, Pittsburgh, PA 15213.

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