Best practices for high data-rate macromolecular crystallography (HDRMX)

Herbert J Bernstein; Lawrence C Andrews; Jorge A Diaz Jr; Jean Jakoncic; Thu Nguyen; Nicholas K Sauter; Alexei S Soares; Justin Y Wei; Maciej R Wlodek; Mario A Xerri

doi:10.1063/1.5128498

Best practices for high data-rate macromolecular crystallography (HDRMX)

Struct Dyn. 2020 Jan 9;7(1):014302. doi: 10.1063/1.5128498. eCollection 2020 Jan.

Authors

Affiliations

¹ Ronin Institute for Independent Scholarship, c/o NSLS-II Bldg 745, Brookhaven National Laboratory, Upton, New York 11973, USA.
² Ronin Institute for Independent Scholarship, 9515 NE 137th St., Kirkland, Washington 98034, USA.
³ Brookhaven National Laboratory, NSLS-II Bldg 745, Upton, New York 11973, USA.
⁴ Stony Brook University, Stony Brook, New York 11794, USA.
⁵ Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, California 94720, USA.
⁶ Mount Sinai High School, 110 N Country Rd., Mt Sinai, New York 11766, USA.

Abstract

In macromolecular crystallography, higher flux, smaller beams, and faster detectors open the door to experiments with very large numbers of very small samples that can reveal polymorphs and dynamics but require re-engineering of approaches to the clustering of images both at synchrotrons and XFELs (X-ray free electron lasers). The need for the management of orders of magnitude more images and limitations of file systems favor a transition from simple one-file-per-image systems such as CBF to image container systems such as HDF5. This further increases the load on computers and networks and requires a re-examination of the presentation of metadata. In this paper, we discuss three important components of this problem-improved approaches to the clustering of images to better support experiments on polymorphs and dynamics, recent and upcoming changes in metadata for Eiger images, and software to rapidly validate images in the revised Eiger format.

Abstract

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