Suppr超能文献

快速测定复杂生物样品中的四级蛋白质结构。

Rapid determination of quaternary protein structures in complex biological samples.

机构信息

Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Klinikgatan 32, SE-22184, Lund, Sweden.

S3IT, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.

出版信息

Nat Commun. 2019 Jan 14;10(1):192. doi: 10.1038/s41467-018-07986-1.

Abstract

The understanding of complex biological systems is still hampered by limited knowledge of biologically relevant quaternary protein structures. Here, we demonstrate quaternary structure determination in biological samples using a combination of chemical cross-linking, high-resolution mass spectrometry and high-accuracy protein structure modeling. This approach, termed targeted cross-linking mass spectrometry (TX-MS), relies on computational structural models to score sets of targeted cross-linked peptide signals acquired using a combination of mass spectrometry acquisition techniques. We demonstrate the utility of TX-MS by creating a high-resolution quaternary model of a 1.8 MDa protein complex composed of a pathogen surface protein and ten human plasma proteins. The model is based on a dense network of cross-link distance constraints obtained directly in a mixture of human plasma and live bacteria. These results demonstrate that TX-MS can increase the applicability of flexible backbone docking algorithms to large protein complexes by providing rich cross-link distance information from complex biological samples.

摘要

复杂生物系统的理解仍然受到对生物相关四级蛋白质结构的有限知识的阻碍。在这里,我们使用化学交联、高分辨率质谱和高精度蛋白质结构建模的组合来展示生物样品中的四级结构测定。这种方法称为靶向交联质谱 (TX-MS),依赖于计算结构模型来对使用多种质谱采集技术获得的靶向交联肽信号集进行评分。我们通过创建一个由病原体表面蛋白和十种人类血浆蛋白组成的 1.8 MDa 蛋白质复合物的高分辨率四级模型来证明 TX-MS 的实用性。该模型基于在人血浆和活细菌混合物中直接获得的交联距离约束的密集网络。这些结果表明,TX-MS 可以通过提供来自复杂生物样品的丰富交联距离信息,增加灵活骨架对接算法对大蛋白质复合物的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/72e6/6331586/d5ca7d193551/41467_2018_7986_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验