用于生物物理分析和超分子环境的亚层析成像平均

IF 3.5 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Structural Biology: X Pub Date : 2022-01-01 DOI:10.1016/j.yjsbx.2022.100076
Lauren Ann Metskas , Rosalie Wilfong , Grant J. Jensen
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引用次数: 5

摘要

最近硬件、软件和计算能力的进步使得低温电子断层扫描和亚层析成像平均的应用越来越广泛。现在有可能在这些实验中揭示结构和生物物理关系,如蛋白质结合伙伴和小分子占用。然而,一些数据处理选择要求用户优先考虑结构或生物物理背景。在这里,我们提出了一种改进的亚层析图平均方法,保留了这两种能力。通过提高粒子拾取的准确性,对所有子层析图进行对齐和平均,减少对对称性和紧掩膜的依赖,极大地提高了生物物理分析的断层扫描和子层析图平均数据的可用性,而不会对结构改进产生负面影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Subtomogram averaging for biophysical analysis and supramolecular context

Recent advances in hardware, software and computing power have led to increasingly ambitious applications of cryo-electron tomography and subtomogram averaging. It is now possible to reveal both structures and biophysical relationships like protein binding partners and small molecule occupancy in these experiments. However, some data processing choices require the user to prioritize structure or biophysical context. Here, we present a modified subtomogram averaging approach that preserves both capabilities. By increasing the accuracy of particle-picking, performing alignment and averaging on all subtomograms, and decreasing reliance on symmetry and tight masks, the usability of tomography and subtomogram averaging data for biophysical analyses is greatly increased without negatively impacting structural refinements.

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来源期刊
Journal of Structural Biology: X
Journal of Structural Biology: X Biochemistry, Genetics and Molecular Biology-Structural Biology
CiteScore
6.50
自引率
0.00%
发文量
20
审稿时长
62 days
期刊最新文献
Corrigendum to “Minimizing ice contamination during specimen preparation for cryo-soft X-ray tomography and cryo-electron tomography” [J. Struct. Biol.: X 10(2024) 100113] Structural analysis of the stable form of fibroblast growth factor 2 – FGF2-STAB Localization of albumin with correlative super resolution light- and electron microscopy in the kidney Minimizing ice contamination during specimen preparation for cryo-soft X-ray tomography and cryo-electron tomography Assessment of submicron bone tissue composition in plastic-embedded samples using optical photothermal infrared (O-PTIR) spectral imaging and machine learning
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