Research Article: ZOO: an automatic data-collection system for high-throughput structure analysis in protein microcrystallography

Date Published: February 01, 2019

Publisher: International Union of Crystallography

Author(s): Kunio Hirata, Keitaro Yamashita, Go Ueno, Yoshiaki Kawano, Kazuya Hasegawa, Takashi Kumasaka, Masaki Yamamoto.

http://doi.org/10.1107/S2059798318017795

Abstract

An automated data-collection system named ZOO has been developed. This system enabled faster data collection, facilitated advanced data-collection and data-processing techniques, and permitted the collection of higher quality data.

Partial Text

The elucidation of high-resolution structures of biological macromolecules has contributed greatly to our understanding of biological processes at the molecular level. Although macromolecular crystallography is a powerful technique, it is sometimes difficult to crystallize important targets, such as macromolecular complexes and membrane proteins, for basic and applied sciences. Recently, the small and brilliant X-ray beams that are now available at synchrotron facilities have enabled the structural analyses of difficult proteins, even when only poorly diffracting crystals are available (Smith et al., 2012 ▸).

In this report, we describe an automated data-collection system, ZOO, and evaluate its ability to analyze various experimental schemes. The ZOO system successfully automated all possible goniometer-based data-collection protocols. Moreover, the ZOO system dramatically shortened the time needed for data collection by using the ‘fast raster scan’ system and the fast spot-finder program SHIKA. Additionally, ZooNavigator smoothly connected the experimental sequence without any time gaps; for example, time to input commands for the beamline GUI. KUMA also reduces the time required for considering suitable exposure conditions.

Currently, ZOO continues data collection until it is stopped or until all user-defined experiments are finalized. When the time for data collection is not sufficient, all data collection cannot be completed. Thus, it is preferable to detect ‘data completion’ for ongoing samples in ZOO experiments. For example, if ZooNavigator can detect data completion by communicating with KAMO, it is possible to automatically stop the measurements and start data collection for the next sample. Although HITO can automatically select a data-collection scheme according to the crystal size and the spatial relationships among the crystals, it still requires user-defined parameters. Future development involves the implementation of more intelligent functions, such as the distinction of each crystal orientation or crystal overlap (Melnikov et al., 2018 ▸), in ZOO to eliminate user-defined parameters.

The source code for the Cheetah client in SHIKA is available under a GPL license at the GitHub website (https://github.com/keitaroyam/cheetah/tree/eiger-zmq). The automatic data-processing system KAMO is available under the new BSD license at the GitHub website (https://github.com/keitaroyam/yamtbx), where the remaining parts of SHIKA will also become available in the near future.

 

Source:

http://doi.org/10.1107/S2059798318017795

 

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