SPIND: a reference-based auto-indexing algorithm for sparse serial crystallography data
Category
Published on
Type
journal-article
Author
Chufeng Li and Xuanxuan Li and Richard Kirian and John C. H. Spence and Haiguang Liu and Nadia A. Zatsepin
Citation
Li, C. et al., 2019. SPIND: a reference-based auto-indexing algorithm for sparse serial crystallography data. IUCrJ, 6(1), pp.72–84. Available at: http://dx.doi.org/10.1107/s2052252518014951.
Abstract
SPIND (sparse-pattern indexing) is an auto-indexing algorithm for sparse snapshot diffraction patterns (`stills') that requires the positions of only five Bragg peaks in a single pattern, when provided with unit-cell parameters. The capability of SPIND is demonstrated for the orientation determination of sparse diffraction patterns using simulated data from microcrystals of a small inorganic molecule containing three iodines, 5-amino-2,4,6-triiodoisophthalic acid monohydrate (I3C) [Beck & Sheldrick (2008), Acta Cryst. E64, o1286], which is challenging for commonly used indexing algorithms. SPIND, integrated with CrystFEL [White et al. (2012), J. Appl. Cryst.
45, 335–341], is then shown to improve the indexing rate and quality of merged serial femtosecond crystallography data from two membrane proteins, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2 and the NTQ chloride-pumping rhodopsin (CIR). The study demonstrates the suitability of SPIND for indexing sparse inorganic crystal data with smaller unit cells, and for improving the quality of serial femtosecond protein crystallography data, significantly reducing the amount of sample and beam time required by making better use of limited data sets. SPIND is written in Python and is publicly available under the GNU General Public License from https://github.com/LiuLab-CSRC/SPIND.
DOI
Funding
NSF-STC Biology with X-ray Lasers (NSF-1231306)
