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Title: SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data

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. E 64 , 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-NH 2 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.


Citation Formats

Li, Chufeng, Li, Xuanxuan, Kirian, Richard, Spence, John C. H., Liu, Haiguang, and Zatsepin, Nadia A. SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data. United Kingdom: N. p., 2019. Web. doi:10.1107/S2052252518014951.
Li, Chufeng, Li, Xuanxuan, Kirian, Richard, Spence, John C. H., Liu, Haiguang, & Zatsepin, Nadia A. SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data. United Kingdom. doi:10.1107/S2052252518014951.
Li, Chufeng, Li, Xuanxuan, Kirian, Richard, Spence, John C. H., Liu, Haiguang, and Zatsepin, Nadia A. Tue . "SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data". United Kingdom. doi:10.1107/S2052252518014951.
@article{osti_1485185,
title = {SPIND : a reference-based auto-indexing algorithm for sparse serial crystallography data},
author = {Li, Chufeng and Li, Xuanxuan and Kirian, Richard and Spence, John C. H. and Liu, Haiguang and Zatsepin, Nadia A.},
abstractNote = {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. E 64 , 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-NH 2 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 = {10.1107/S2052252518014951},
journal = {IUCrJ},
number = 1,
volume = 6,
place = {United Kingdom},
year = {2019},
month = {1}
}

Journal Article:
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DOI: 10.1107/S2052252518014951

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