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活细胞中的共价蛋白质标记定量结构蛋白质组学。

Quantitative structural proteomics in living cells by covalent protein painting.

机构信息

Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States.

Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, United States.

出版信息

Methods Enzymol. 2023;679:33-63. doi: 10.1016/bs.mie.2022.08.046. Epub 2023 Jan 4.

Abstract

The fold and conformation of proteins are key to successful cellular function, but all techniques for protein structure determination are performed in an artificial environment with highly purified proteins. While protein conformations have been solved to atomic resolution and modern protein structure prediction tools rapidly generate near accurate models of proteins, there is an unmet need to uncover the conformations of proteins in living cells. Here, we describe Covalent Protein Painting (CPP), a simple and fast method to infer structural information on protein conformation in cells with a quantitative protein footprinting technology. CPP monitors the conformational landscape of the 3D proteome in cells with high sensitivity and throughput. A key advantage of CPP is its' ability to quantitatively compare the 3D proteomes between different experimental conditions and to discover significant changes in the protein conformations. We detail how to perform a successful CPP experiment, the factors to consider before performing the experiment, and how to interpret the results.

摘要

蛋白质的折叠和构象是细胞功能成功的关键,但所有用于确定蛋白质结构的技术都是在高度纯化的蛋白质的人工环境中进行的。虽然已经解析了原子分辨率的蛋白质构象,并且现代蛋白质结构预测工具可以快速生成接近准确的蛋白质模型,但仍需要揭示活细胞中蛋白质的构象。在这里,我们描述了共价蛋白质作图(CPP),这是一种使用定量蛋白质足迹技术推断细胞中蛋白质构象结构信息的简单快速方法。CPP 以高灵敏度和高通量监测细胞中 3D 蛋白质组的构象景观。CPP 的一个关键优势是能够定量比较不同实验条件下的 3D 蛋白质组,并发现蛋白质构象的显著变化。我们详细介绍了如何进行成功的 CPP 实验,在进行实验之前需要考虑的因素,以及如何解释结果。

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1
Cancer Conformational Landscape Shapes Tumorigenesis.
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2
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3
Thermal proteome profiling for interrogating protein interactions.
Mol Syst Biol. 2020 Mar;16(3):e9232. doi: 10.15252/msb.20199232.
4
Fast photochemical oxidation of proteins (FPOP): A powerful mass spectrometry-based structural proteomics tool.
J Biol Chem. 2019 Aug 9;294(32):11969-11979. doi: 10.1074/jbc.REV119.006218. Epub 2019 Jul 1.
5
UniProt: a worldwide hub of protein knowledge.
Nucleic Acids Res. 2019 Jan 8;47(D1):D506-D515. doi: 10.1093/nar/gky1049.
6
Deducing the presence of proteins and proteoforms in quantitative proteomics.
Nat Commun. 2018 Jun 13;9(1):2320. doi: 10.1038/s41467-018-04411-5.
7
Platforms and Pipelines for Proteomics Data Analysis and Management.
Adv Exp Med Biol. 2016;919:203-215. doi: 10.1007/978-3-319-41448-5_9.
8
A comprehensive and scalable database search system for metaproteomics.
BMC Genomics. 2016 Aug 16;17(1):642. doi: 10.1186/s12864-016-2855-3.
10
ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity.
J Proteomics. 2015 Nov 3;129:16-24. doi: 10.1016/j.jprot.2015.07.001. Epub 2015 Jul 11.

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