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一种用于稀疏串行晶体学数据的基于参考的自动索引算法。

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

作者信息

Li Chufeng, Li Xuanxuan, Kirian Richard, Spence John C H, Liu Haiguang, Zatsepin Nadia A

机构信息

Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.

Center for Applied Structural Discovery, The Biodesign Institute, Arizona State University, Tempe, Arizona 85287, USA.

出版信息

IUCrJ. 2019 Jan 1;6(Pt 1):72-84. doi: 10.1107/S2052252518014951.

Abstract

(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 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 ▸), E, o1286], which is challenging for commonly used indexing algorithms. , integrated with [White (2012 ▸), , 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 and the NTQ chloride-pumping rhodopsin (CIR). The study demonstrates the suitability of 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. is written in Python and is publicly available under the GNU General Public License from https://github.com/LiuLab-CSRC/SPIND.

摘要

(稀疏模式索引)是一种用于稀疏快照衍射图案(“静态图”)的自动索引算法,当给定晶胞参数时,该算法仅需要单个图案中五个布拉格峰的位置。使用来自含有三个碘的小分子无机晶体5-氨基-2,4,6-三碘间苯二甲酸一水合物(I3C)[Beck & Sheldrick (2008 ▸), E, o1286]的模拟数据,展示了该算法在确定稀疏衍射图案取向方面的能力,这对于常用的索引算法来说具有挑战性。然后,将该算法与[White (2012 ▸),, 335 - 341]集成,结果表明其提高了来自两种膜蛋白(与双功能肽配体DIPP - NH复合的人δ-阿片受体和NTQ氯化物泵浦视紫红质(CIR))的合并串行飞秒晶体学数据的索引速率和质量。该研究证明了该算法适用于索引具有较小晶胞的稀疏无机晶体数据,以及提高串行飞秒蛋白质晶体学数据的质量,通过更好地利用有限数据集显著减少了所需的样品量和束流时间。该算法用Python编写,可根据GNU通用公共许可证从https://github.com/LiuLab-CSRC/SPIND公开获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2891/6327178/4cdf83b42b23/m-06-00072-fig1.jpg

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