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arXiv · 2110.07608

3D Structure from 2D Microscopy images using Deep Learning

Benjamin J. Blundell·Christian Sieben·Suliana Manley·Ed Rosten·QueeLim Ch'ng·Susan Cox
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Abstract

Understanding the structure of a protein complex is crucial indetermining 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. Herewe present a deep learning solution for reconstructing the protein com-plexes 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 dis-carded, 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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Benjamin J. Blundell, Christian Sieben, Suliana Manley, Ed Rosten, QueeLim Ch'ng, Susan Cox. 2021-10-14. 3D Structure from 2D Microscopy images using Deep Learning. https://doi.org/10.3389/fbinf.2021.740342

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