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基于有限酶解和交联的质谱结构蛋白质组学

Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross-Linking.

作者信息

Lu Haiyan, Zhu Zexin, Fields Lauren, Zhang Hua, Li Lingjun

机构信息

School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA.

出版信息

Mass Spectrom Rev. 2024 Sep 19. doi: 10.1002/mas.21908.

Abstract

The exploration of protein structure and function stands at the forefront of life science and represents an ever-expanding focus in the development of proteomics. As mass spectrometry (MS) offers readout of protein conformational changes at both the protein and peptide levels, MS-based structural proteomics is making significant strides in the realms of structural and molecular biology, complementing traditional structural biology techniques. This review focuses on two powerful MS-based techniques for peptide-level readout, namely limited proteolysis-mass spectrometry (LiP-MS) and cross-linking mass spectrometry (XL-MS). First, we discuss the principles, features, and different workflows of these two methods. Subsequently, we delve into the bioinformatics strategies and software tools used for interpreting data associated with these protein conformation readouts and how the data can be integrated with other computational tools. Furthermore, we provide a comprehensive summary of the noteworthy applications of LiP-MS and XL-MS in diverse areas including neurodegenerative diseases, interactome studies, membrane proteins, and artificial intelligence-based structural analysis. Finally, we discuss the factors that modulate protein conformational changes. We also highlight the remaining challenges in understanding the intricacies of protein conformational changes by LiP-MS and XL-MS technologies.

摘要

蛋白质结构与功能的探索处于生命科学的前沿,是蛋白质组学发展中一个不断扩展的重点领域。由于质谱(MS)能够在蛋白质和肽水平上读出蛋白质构象变化,基于质谱的结构蛋白质组学在结构生物学和分子生物学领域正取得重大进展,对传统结构生物学技术起到补充作用。本综述聚焦于两种用于肽水平读出且功能强大的基于质谱的技术,即有限蛋白酶解质谱(LiP-MS)和交联质谱(XL-MS)。首先,我们讨论这两种方法的原理、特点及不同工作流程。随后,我们深入探讨用于解释与这些蛋白质构象读出相关数据的生物信息学策略和软件工具,以及这些数据如何与其他计算工具整合。此外,我们全面总结了LiP-MS和XL-MS在神经退行性疾病、相互作用组研究、膜蛋白以及基于人工智能的结构分析等不同领域的显著应用。最后,我们讨论调节蛋白质构象变化的因素。我们还强调了通过LiP-MS和XL-MS技术理解蛋白质构象变化复杂性方面仍然存在的挑战。

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