Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA; email:
Annu Rev Phys Chem. 2022 Apr 20;73:1-19. doi: 10.1146/annurev-physchem-082720-123928. Epub 2021 Nov 1.
Knowledge of protein structure is crucial to our understanding of biological function and is routinely used in drug discovery. High-resolution techniques to determine the three-dimensional atomic coordinates of proteins are available. However, such methods are frequently limited by experimental challenges such as sample quantity, target size, and efficiency. Structural mass spectrometry (MS) is a technique in which structural features of proteins are elucidated quickly and relatively easily. Computational techniques that convert sparse MS data into protein models that demonstrate agreement with the data are needed. This review features cutting-edge computational methods that predict protein structure from MS data such as chemical cross-linking, hydrogen-deuterium exchange, hydroxyl radical protein footprinting, limited proteolysis, ion mobility, and surface-induced dissociation. Additionally, we address future directions for protein structure prediction with sparse MS data.
了解蛋白质结构对于我们理解生物功能至关重要，并且经常用于药物发现。有多种高分辨率技术可用于确定蛋白质的三维原子坐标。然而，这些方法经常受到实验挑战的限制，例如样品数量、目标大小和效率。结构质谱（MS）是一种能够快速、相对容易地阐明蛋白质结构特征的技术。需要计算技术将稀疏的 MS 数据转换为与数据一致的蛋白质模型。本综述介绍了从化学交联、氢氘交换、羟基自由基蛋白足迹、有限蛋白水解、离子淌度和表面诱导解离等 MS 数据预测蛋白质结构的前沿计算方法。此外，我们还讨论了使用稀疏 MS 数据进行蛋白质结构预测的未来方向。
