AUTHOR=Blundell Benjamin , Sieben Christian , Manley Suliana , Rosten Ed , Ch’ng QueeLim , Cox Susan TITLE=3D Structure From 2D Microscopy Images Using Deep Learning JOURNAL=Frontiers in Bioinformatics VOLUME=1 YEAR=2021 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2021.740342 DOI=10.3389/fbinf.2021.740342 ISSN=2673-7647 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.