Frontiers in Bioinformatics (Oct 2021)

3D Structure From 2D Microscopy Images Using Deep Learning

  • Benjamin Blundell,
  • Christian Sieben,
  • Suliana Manley,
  • Ed Rosten,
  • QueeLim Ch’ng,
  • Susan Cox

DOI
https://doi.org/10.3389/fbinf.2021.740342
Journal volume & issue
Vol. 1

Abstract

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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.

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