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用于非晶态生物分子衍射成像的模拟软件包。

: a simulation package for diffractive imaging of noncrystalline biomolecules.

作者信息

Peck Ariana, Chang Hsing-Yin, Dujardin Antoine, Ramalingam Deeban, Uervirojnangkoorn Monarin, Wang Zhaoyou, Mancuso Adrian, Poitevin Frédéric, Yoon Chun Hong

机构信息

Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA.

European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany.

出版信息

J Appl Crystallogr. 2022 Jul 15;55(Pt 4):1002-1010. doi: 10.1107/S1600576722005994. eCollection 2022 Aug 1.

DOI:10.1107/S1600576722005994
PMID:35974743
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9348890/
Abstract

X-ray free-electron lasers (XFELs) have the ability to produce ultra-bright femtosecond X-ray pulses for coherent diffraction imaging of biomolecules. While the development of methods and algorithms for macromolecular crystallography is now mature, XFEL experiments involving aerosolized or solvated biomolecular samples offer new challenges in terms of both experimental design and data processing. is a simulation package that can generate single-hit diffraction images for reconstruction algorithms, multi-hit diffraction images of aggregated particles for training machine learning classifiers using labeled data, diffraction images of randomly distributed particles for fluctuation X-ray scattering algorithms, and diffraction images of reference and target particles for holographic reconstruction algorithms. is a resource to aid feasibility studies and advance the development of algorithms for noncrystalline experiments at XFEL facilities.

摘要

X射线自由电子激光器(XFELs)能够产生超亮的飞秒X射线脉冲,用于生物分子的相干衍射成像。虽然大分子晶体学的方法和算法现在已经成熟,但涉及雾化或溶剂化生物分子样品的XFEL实验在实验设计和数据处理方面都带来了新的挑战。[具体软件名称]是一个模拟软件包,它可以生成用于重建算法的单次衍射图像、用于使用标记数据训练机器学习分类器的聚集颗粒的多次衍射图像、用于波动X射线散射算法的随机分布颗粒的衍射图像,以及用于全息重建算法的参考和目标颗粒的衍射图像。[具体软件名称]是一种资源,有助于可行性研究,并推动XFEL设施中非晶体实验算法的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/04c879dab1ed/j-55-01002-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/f0f8a0a9ea0a/j-55-01002-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/060840be6b44/j-55-01002-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/04c879dab1ed/j-55-01002-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/f0f8a0a9ea0a/j-55-01002-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/060840be6b44/j-55-01002-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f776/9348890/04c879dab1ed/j-55-01002-fig3.jpg

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本文引用的文献

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Nat Methods. 2021 Aug;18(8):930-936. doi: 10.1038/s41592-021-01220-5. Epub 2021 Jul 29.
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Selecting XFEL single-particle snapshots by geometric machine learning.通过几何机器学习选择X射线自由电子激光单粒子快照
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3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM.
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CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks.CryoDRGN:使用神经网络重建异质冷冻电镜结构。
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Perspectives on single particle imaging with x rays at the advent of high repetition rate x-ray free electron laser sources.高重复率X射线自由电子激光源出现之际对X射线单粒子成像的展望。
Struct Dyn. 2020 Aug 6;7(4):040901. doi: 10.1063/4.0000024. eCollection 2020 Jul.
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Structural Heterogeneity in Single Particle Imaging Using X-ray Lasers.利用 X 射线激光进行单颗粒成像的结构异质性。
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