Suppr超能文献

通过使用多种蛋白酶和尺寸排阻色谱法进行富集来扩展化学交联工具箱。

Expanding the chemical cross-linking toolbox by the use of multiple proteases and enrichment by size exclusion chromatography.

机构信息

Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zurich, Wolfgang-Pauli-Strasse 16, 8093 Zurich, Switzerland.

出版信息

Mol Cell Proteomics. 2012 Mar;11(3):M111.014126. doi: 10.1074/mcp.M111.014126. Epub 2012 Jan 27.

Abstract

Chemical cross-linking in combination with mass spectrometric analysis offers the potential to obtain low-resolution structural information from proteins and protein complexes. Identification of peptides connected by a cross-link provides direct evidence for the physical interaction of amino acid side chains, information that can be used for computational modeling purposes. Despite impressive advances that were made in recent years, the number of experimentally observed cross-links still falls below the number of possible contacts of cross-linkable side chains within the span of the cross-linker. Here, we propose two complementary experimental strategies to expand cross-linking data sets. First, enrichment of cross-linked peptides by size exclusion chromatography selects cross-linked peptides based on their higher molecular mass, thereby depleting the majority of unmodified peptides present in proteolytic digests of cross-linked samples. Second, we demonstrate that the use of proteases in addition to trypsin, such as Asp-N, can additionally boost the number of observable cross-linking sites. The benefits of both SEC enrichment and multiprotease digests are demonstrated on a set of model proteins and the improved workflow is applied to the characterization of the 20S proteasome from rabbit and Schizosaccharomyces pombe.

摘要

化学交联与质谱分析相结合,为从蛋白质和蛋白质复合物中获取低分辨率结构信息提供了可能。鉴定交联连接的肽段为氨基酸侧链的物理相互作用提供了直接证据,这些信息可用于计算建模目的。尽管近年来取得了令人瞩目的进展,但实验观察到的交联数量仍低于交联侧链在交联剂范围内可能的接触数量。在这里,我们提出了两种互补的实验策略来扩展交联数据集。首先,通过尺寸排阻色谱法对交联肽进行富集,根据其较高的分子量选择交联肽,从而耗尽交联样品酶解物中存在的大多数未修饰肽。其次,我们证明了除胰蛋白酶外,使用蛋白酶(如 Asp-N)还可以额外增加可观察到的交联位点数量。在一组模型蛋白上证明了 SEC 富集和多蛋白酶消化的好处,并将改进的工作流程应用于兔和裂殖酵母 20S 蛋白酶体的表征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eb1/3316732/bd8465d9bcca/zjw0051241300001.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