RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps.

RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps.
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DOI:
10.1038/nmeth.4340
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发表时间:
2017-08
期刊:
影响因子:
48
通讯作者:
DiMaio F
DiMaio F
中科院分区:
生物学1区
文献类型:
--
作者:
Frenz B;Walls AC;Egelman EH;Veesler D;DiMaio F

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RosettaES是一种使用基于片段的采样策略的算法,可在3-5 μ m分辨率下从冷冻EM数据中改进大分子结构建模。本文的在线版本(doi:10.1038/nmeth.4340)包含补充材料,可供授权用户使用。在低温电子显微镜(cryo-EM)图中精确地对大分子结构进行原子建模是一项重大挑战,因为中等分辨率使得原子的精确放置变得困难。我们提出了Rosetta计数采样(RosettaES),一个自动化的工具,使用基于片段的采样策略从头模型完成的大分子结构从冷冻EM密度图在3-5-μ m的分辨率。在9种蛋白质的基准组上,RosettaES能够识别85%片段中的近天然构象。RosettaES还用于确定三种具有挑战性的大分子结构的模型。本文的在线版本(doi:10.1038/nmeth.4340)包含补充材料,可供授权用户使用。
RosettaES, an algorithm that uses a fragment-based sampling strategy, improves macromolecular structure modeling from cryo-EM data at 3–5-Å resolution. The online version of this article (doi:10.1038/nmeth.4340) contains supplementary material, which is available to authorized users. Accurate atomic modeling of macromolecular structures into cryo-electron microscopy (cryo-EM) maps is a major challenge, as the moderate resolution makes accurate placement of atoms difficult. We present Rosetta enumerative sampling (RosettaES), an automated tool that uses a fragment-based sampling strategy for de novo model completion of macromolecular structures from cryo-EM density maps at 3–5-Å resolution. On a benchmark set of nine proteins, RosettaES was able to identify near-native conformations in 85% of segments. RosettaES was also used to determine models for three challenging macromolecular structures. The online version of this article (doi:10.1038/nmeth.4340) contains supplementary material, which is available to authorized users.
DOI: 10.1016/j.ultramic.2009.04.002
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