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从串行飞秒晶体学索引淀粉样肽衍射:稀疏模式的新算法

Indexing amyloid peptide diffraction from serial femtosecond crystallography: new algorithms for sparse patterns.

作者信息

Brewster Aaron S, Sawaya Michael R, Rodriguez Jose, Hattne Johan, Echols Nathaniel, McFarlane Heather T, Cascio Duilio, Adams Paul D, Eisenberg David S, Sauter Nicholas K

机构信息

Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, CA 90095-1570, USA.

出版信息

Acta Crystallogr D Biol Crystallogr. 2015 Feb;71(Pt 2):357-66. doi: 10.1107/S1399004714026145. Epub 2015 Jan 23.

Abstract

Still diffraction patterns from peptide nanocrystals with small unit cells are challenging to index using conventional methods owing to the limited number of spots and the lack of crystal orientation information for individual images. New indexing algorithms have been developed as part of the Computational Crystallography Toolbox (cctbx) to overcome these challenges. Accurate unit-cell information derived from an aggregate data set from thousands of diffraction patterns can be used to determine a crystal orientation matrix for individual images with as few as five reflections. These algorithms are potentially applicable not only to amyloid peptides but also to any set of diffraction patterns with sparse properties, such as low-resolution virus structures or high-throughput screening of still images captured by raster-scanning at synchrotron sources. As a proof of concept for this technique, successful integration of X-ray free-electron laser (XFEL) data to 2.5 Å resolution for the amyloid segment GNNQQNY from the Sup35 yeast prion is presented.

摘要

由于单位晶胞较小的肽纳米晶体的衍射图样中的斑点数量有限,且单个图像缺乏晶体取向信息,因此使用传统方法对其进行指标化具有挑战性。作为计算晶体学工具箱(cctbx)的一部分,已经开发了新的指标化算法来克服这些挑战。从数千个衍射图样的汇总数据集中获得的准确晶胞信息可用于确定单个图像的晶体取向矩阵,所需的反射数量少至五个。这些算法不仅可能适用于淀粉样肽,还适用于任何具有稀疏特性的衍射图样集,例如低分辨率病毒结构或同步加速器源处通过光栅扫描捕获的静态图像的高通量筛选。作为该技术的概念验证,展示了将来自Sup35酵母朊病毒的淀粉样片段GNNQQNY的X射线自由电子激光(XFEL)数据成功整合至2.5 Å分辨率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9846/4321489/f51c26a6784f/d-71-00357-fig1.jpg

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