Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry.

Modeling Protein Complexes Using Restraints from Crosslinking Mass Spectrometry.
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DOI:
10.1016/j.str.2018.04.016
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发表时间:
2018-07-03
期刊:
Structure (London, England : 1993)
影响因子:
--
通讯作者:
Topf M
Topf M
中科院分区:
其他
文献类型:
--
作者:
Bullock JMA;Sen N;Thalassinos K;Topf M

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利用交联质谱(XL-MS)中的约束条件对大分子组件进行建模往往只关注距离破坏。最近,我们确定了交联剂数据中固有的三个不同的建模特征:(1)交联剂残基之间的预期距离;(2)违反交联剂的最大结合;(3)交联剂残基的溶剂可及性。在这里,我们在一个评分函数中实现这些功能。CMNXL,并证明了它的性能优于通常使用的交联链距离违规。我们比较了不同的计算交联残基之间距离的方法,与溶剂可达表面距离相比,欧氏距离在性能上没有明显的变化。最后,我们创建了一个结合了来自3D电子显微镜地图以及交联剂的信息的综合得分。平均而言,这比单独使用任何一种信息类型都获得了更好的结果,并展示了使用XL-MS和低分辨率冷冻电子显微镜进行综合建模的潜力。一种性能优于更常用的交联评分方法的评分函数结合低分辨率3D-EM和交联信息计算交联残基之间距离的方法的比较改进了Bullock等人的评分。提出了一种基于交联链数据(CMNXL)对蛋白质复合体进行建模的评分功能,其性能优于目前使用的其他功能。他们还将这种方法与3D-EM拟合得分相结合,创建了一个综合得分函数,该函数的表现优于单独的每个得分。
Modeling macromolecular assemblies with restraints from crosslinking mass spectrometry (XL-MS) tends to focus solely on distance violation. Recently, we identified three different modeling features inherent in crosslink data: (1) expected distance between crosslinked residues; (2) violation of the crosslinker's maximum bound; and (3) solvent accessibility of crosslinked residues. Here, we implement these features in a scoring function. cMNXL, and demonstrate that it outperforms the commonlyused crosslink distance violation. We compare the different methods of calculating the distance between crosslinked residues, which shows no significant change in performance when using Euclidean distance compared with the solvent-accessible surface distance. Finally, we create a combined score that incorporates information from 3D electron microscopy maps as well as crosslinking. This achieves, on average, better results than either information type alone and demonstrates the potential of integrative modeling with XL-MS and low-resolution cryoelectron microscopy. A scoring function that outperforms more commonly used crosslink scoring methods A comparison of methods to calculate the distance between crosslinked residues Combining low-resolution 3D-EM and crosslinking information improves scoring Bullock et al. present a scoring function for modeling protein complexes based on crosslink data (cMNXL) that outperforms other functions currently in use. They also combine this method with a 3D-EM fitting score to create a combined scoring function that outperforms each score alone.
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