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Learning To Automate Cryo-Electron Microscopy Data Collection With Ptolemy

Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as a primary method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. Automated approaches to improve throughput and efficiency while lowering costs are needed to meet the increasing demand for cryo-EM. Currently, in the process of collecting high-magnification cryo-EM micrographs, data collection requires human input and manual tuning of parameters, as expert operators must navigate low- and medium-magnification images to find good high-magnification collection locations. Automating this is non-trivial: the images suffer from a low signal-to-noise ratio and are affected by a range of experimental parameters that differ for each collection session.

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