Open AccessDeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography
Author(s) -
Ito Sho,
Ueno Go,
Yamamoto Masaki
Publication year - 2019
Publication title -
journal of synchrotron radiation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.172
H-Index - 99
ISSN - 1600-5775
DOI - 10.1107/s160057751900434x
Subject(s) - convolutional neural network , beamline , crystal (programming language) , throughput , synchrotron , computer science , deep learning , crystallography , process (computing) , artificial intelligence , materials science , chemistry , physics , optics , telecommunications , beam (structure) , wireless , programming language , operating system
High‐throughput protein crystallography using a synchrotron light source is an important method used in drug discovery. Beamline components for automated experiments including automatic sample changers have been utilized to accelerate the measurement of a number of macromolecular crystals. However, unlike cryo‐loop centering, crystal centering involving automated crystal detection is a difficult process to automate fully. Here, DeepCentering, a new automated crystal centering system, is presented. DeepCentering works using a convolutional neural network, which is a deep learning operation. This system achieves fully automated accurate crystal centering without using X‐ray irradiation of crystals, and can be used for fully automated data collection in high‐throughput macromolecular crystallography.