Deep Learning Improves Macromolecule Identification in 3D Cellular Cryo-Electron Tomograms

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Moebel, Emmanuel | Martinez-Sanchez, Antonio | Lamm, Lorenz | Righetto, Ricardo, D | Wietrzynski, Wojciech | Albert, Sahradha | Larivière, Damien | Fourmentin, Eric | Pfeffer, Stefan | Ortiz, Julio | Baumeister, Wolfgang | Peng, Tingying | Engel, Benjamin, D | Kervrann, Charles

Edité par CCSD ; Nature Publishing Group -

International audience. Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (∼3.2 MDa), Rubisco (∼560 kDa soluble complex), and photosystem II (∼550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semi-automated analysis of a wide range of molecular targets in cellular tomograms.

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