Blundell, BenjaminSieben, ChristianManley, SulianaRosten, EdCh'ng, QueelimCox, Susan2024-02-162024-02-162024-02-162021-10-2810.3389/fbinf.2021.740342https://infoscience.epfl.ch/handle/20.500.14299/203889WOS:001085979300001Understanding 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.Life Sciences & BiomedicineSmlmDeep-LearningStructureStormAitext::journal::journal article::research article