3D Structure From 2D Microscopy Images Using Deep Learning.

Journal: Frontiers in bioinformatics
Published Date: Oct 28, 2021

Abstract

Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles.

Authors

  • Benjamin Blundell
    Centre for Developmental Biology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Christian Sieben
    Nanoscale Infection Biology Lab (NIBI), Helmholtz Centre for Infection Research, London, Germany.
  • Suliana Manley
    École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
  • Ed Rosten
    Snap, Inc., London, United Kingdom.
  • QueeLim Ch'ng
    Centre for Developmental Biology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
  • Susan Cox
    Randall Centre for Cell and Molecular Biophysics, King's College London, London, United Kingdom.

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