DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography

J Synchrotron Radiat. 2019 Jul 1;26(Pt 4):1361-1366. doi: 10.1107/S160057751900434X. Epub 2019 Jun 3.

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.

Keywords: automated crystal centering; deep learning; fully automated structure determination.