Hydrogen properties in an organic molecule revealed by XFEL and electron crystallography

Structure analysis of small crystals is important in synthetic organic chemistry, pharmaceutical and material sciences, and related areas, as the conformation of these molecules may differ in large and small crystals, thus affecting the interpretation of their functional properties and drug efficacy. From small crystals, X-ray and electron beams could furnish electron densities and Coulomb potentials of target molecules, respectively. The two beams provide distinctly different information, and this potential has not been fully explored. Here we present the detailed structure of an organic molecule, rhodamine-6g by X-ray free-electron laser (XFEL) and electron crystallography from the same sample batch of microcrystals. This is the first organic molecular structure determined using XFEL at subatomic resolution. Direct comparison between the electron-density and the Coulomb-potential maps together with theoretical models based on Poisson’s equation shows that the position of hydrogen atoms depends on bond type and charge distribution. The combined approach could lead to better insights into their chemical and/or binding properties for a broad range of organic molecules.

Machine learning-based real-time object locator/evaluator for cryo-EM data collection

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation shows its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and in locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection. The proposed approach will advance high-throughput and accurate data collection of images and diffraction patterns with minimal human operation.

Real-time object locator for cryo-EM data collection --- You only navigate EM once ---

In cryo-electron microscopy (cryo-EM) data collection, locating a target object is the most error-prone. Here, we present a machine learning-based approach with a real-time object locator named yoneoLocr using YOLO, a well-known object detection system. Implementation showed its effectiveness in rapidly and precisely locating carbon holes in single particle cryo-EM and for locating crystals and evaluating electron diffraction (ED) patterns in automated cryo-electron crystallography (cryo-EX) data collection.