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

Journal: Communications biology
Published Date:

Abstract

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.

Authors

  • Koji Yonekura
    Biostructural Mechanism Laboratory, RIKEN SPring-8 Center, Sayo, Hyogo, Japan. yone@spring8.or.jp.
  • Saori Maki-Yonekura
    Biostructural Mechanism Laboratory, RIKEN SPring-8 Center, Sayo, Hyogo, Japan.
  • Hisashi Naitow
    Biostructural Mechanism Laboratory, RIKEN SPring-8 Center, Sayo, Hyogo, Japan.
  • Tasuku Hamaguchi
    Biostructural Mechanism Laboratory, RIKEN SPring-8 Center, Sayo, Hyogo, Japan.
  • Kiyofumi Takaba
    Biostructural Mechanism Laboratory, RIKEN SPring-8 Center, Sayo, Hyogo, Japan.