MiLoPYP: self-supervised molecular pattern mining and particle localization in situ Cryo-electron tomography (CET) allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography (SPT), it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with CET/SPT are the automatic identification and…
Automated systematic evaluation of cryo-EM specimens with SmartScope We present SmartScope, the first framework to streamline, standardize, and automate specimen evaluation in cryo-electron microscopy. SmartScope employs deep-learning-based object detection to identify and classify features suitable for imaging, allowing it to perform thorough specimen screening in a fully automated manner. A web interface provides remote control over the automated operation…
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning We present a multiple-image super-resolution (SR) algorithm based on deep internal learning designed specifically to work under low-SNR conditions typical of cryo-EM data. Our approach leverages the internal image statistics of cryo-EM movies and does not require training on ground-truth data. When applied to a single-particle dataset of apoferritin, we show…
Unsupervised particle sorting for high-resolution single-particle cryo-EM Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing…
