Quantitative cross-linking/mass spectrometry using isotope-labelled cross-linkers
Quantitative cross-linking/mass spectrometry using isotope-labelled cross-linkers
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DOI：
10.1016/j.jprot.2013.03.005
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发表时间：
2013-08-02
影响因子：
3.3
通讯作者：
Rappsilber, Jun
中科院分区：
文献类型：
--
作者：
Fischer, Lutz;Chen, Zhuo Angel;Rappsilber, Jun
动态蛋白和多蛋白复合物支配着大多数生物过程。交联/质谱（CLMS）在提供静态蛋白质结构的残留物分辨率数据方面越来越成功。在这里，我们研究了使用同位素标记进行定量记录动态过程的技术可行性。我们将人血清白蛋白（HSA）与现成的交联剂BS3-d0/4以不同的重/轻比交联。我们发现了两个限制。首先，同位素标记减少了鉴定出的交联的数量。这与鉴定蛋白质时的类似发现是一致的。其次，标准的定量蛋白质组学软件不适合交联工作。为了改进这一点，我们编写了一个基本的开源应用程序XiQ。使用XiQ，我们可以确定定量CLMS在技术上是可行的。生物意义交联/质谱法（CLMS）已成为提供静态蛋白质结构残馀分辨率数据的有力工具。在CLMS中加入定量将扩展其记录动态过程的能力。在这里，我们介绍了一种使用同位素标记交联剂的交联特异性定量策略。使用模型系统，我们展示了量化交联数据的原理和可行性，并讨论了在这样做时可能遇到的挑战。然后，我们提供了一个基本的开源应用程序Xig来执行CLMS数据的自动定量。我们的工作为更轻松地研究生物过程的分子细节奠定了基础。这篇文章是《蛋白质组学的新视野和应用》特刊的一部分。(C) 2013 Elsevier B.V.版权所有
Dynamic proteins and multi-protein complexes govern most biological processes. Cross-linking/mass spectrometry (CLMS) is increasingly successful in providing residue-resolution data on static proteinaceous structures. Here we investigate the technical feasibility of recording dynamic processes using isotope-labelling for quantitation. We cross-linked human serum albumin (HSA) with the readily available cross-linker BS3-d0/4 in different heavy/light ratios. We found two limitations. First, isotope labelling reduced the number of identified cross-links. This is in line with similar findings when identifying proteins. Second, standard quantitative proteomics software was not suitable for work with cross-linking. To ameliorate this we wrote a basic open source application, XiQ. Using XiQ we could establish that quantitative CLMS was technically feasible.Biological significanceCross-linking/mass spectrometry (CLMS) has become a powerful tool for providing residue-resolution data on static proteinaceous structures. Adding quantitation to CLMS will extend its ability of recording dynamic processes. Here we introduce a cross-linking specific quantitation strategy by using isotope labelled cross-linkers. Using a model system, we demonstrate the principle and feasibility of quantifying cross-linking data and discuss challenges one may encounter while doing so. We then provide a basic open source application, Xig to carry out automated quantitation of CLMS data. Our work lays the foundations of studying the molecular details of biological processes at greater ease than this could be done so far.This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]. (C) 2013 Elsevier B.V. All rights reserved.