Olerinyova Anna, Sonn-Segev Adar, Gault Joseph, Eichmann Cédric, Schimpf Johannes, Kopf Adrian H, Rudden Lucas S P, Ashkinadze Dzmitry, Bomba Radoslaw, Frey Lukas, Greenwald Jason, Degiacomi Matteo T, Steinhilper Ralf, Killian J Antoinette, Friedrich Thorsten, Riek Roland, Struwe Weston B, Kukura Philipp
Physical and Theoretical Chemistry Laboratory, Department of Chemistry, University of Oxford, South Parks Road, Oxford OX1 3QZ, UK.
Laboratory of Physical Chemistry, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1-5/10, 8093 Zürich, Switzerland.
Chem. 2021 Jan 14;7(1):224-236. doi: 10.1016/j.chempr.2020.11.011.
Integral membrane proteins (IMPs) are biologically highly significant but challenging to study because they require maintaining a cellular lipid-like environment. Here, we explore the application of mass photometry (MP) to IMPs and membrane-mimetic systems at the single-particle level. We apply MP to amphipathic vehicles, such as detergents and amphipols, as well as to lipid and native nanodiscs, characterizing the particle size, sample purity, and heterogeneity. Using methods established for cryogenic electron microscopy, we eliminate detergent background, enabling high-resolution studies of membrane-protein structure and interactions. We find evidence that, when extracted from native membranes using native styrene-maleic acid nanodiscs, the potassium channel KcsA is present as a dimer of tetramers-in contrast to results obtained using detergent purification. Finally, using lipid nanodiscs, we show that MP can help distinguish between functional and non-functional nanodisc assemblies, as well as determine the critical factors for lipid nanodisc formation.
整合膜蛋白（IMPs）在生物学上具有高度重要性，但由于需要维持类似细胞脂质的环境，因此研究起来具有挑战性。在这里，我们探索了单颗粒水平下质量光度法（MP）在整合膜蛋白和膜模拟系统中的应用。我们将MP应用于两亲性载体，如去污剂和两性离子聚合物，以及脂质和天然纳米盘，表征颗粒大小、样品纯度和异质性。使用为低温电子显微镜建立的方法，我们消除了去污剂背景，从而能够对膜蛋白结构和相互作用进行高分辨率研究。我们发现有证据表明，当使用天然苯乙烯-马来酸纳米盘从天然膜中提取时，钾通道KcsA以四聚体二聚体的形式存在，这与使用去污剂纯化获得的结果形成对比。最后，使用脂质纳米盘，我们表明MP可以帮助区分功能性和非功能性纳米盘组装体，并确定脂质纳米盘形成的关键因素。
