  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.
