DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography. (11th June 2019)

• Record Type: Journal Article
• Authors: Ito, Sho; Ueno, Go; Yamamoto, Masaki
• Is Part Of: Journal of synchrotron radiation. Volume 26:Part 4(2019)
• Page Start: 1361
• Page End: 1366
• Publication Date: 2019-06-11
• Subjects: automated crystal centering -- deep learning -- fully automated structure determination; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S160057751900434X
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : A fully automated crystal centering system using deep learning is presented. Using this system, a fully automated crystal structure determination pipeline has also been developed. Abstract : 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.