Grosskopf Julian D, Kasson Peter, Lerch Michael T
Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Departments of Chemistry and Biochemistry and of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
bioRxiv. 2025 Jun 8:2025.06.05.658127. doi: 10.1101/2025.06.05.658127.
Conformational heterogeneity is integral to protein function - ranging from enzyme catalysis to signal transduction - and visualizing distinct conformational states requires experimental techniques capable of providing such structural information. One particularly powerful method, double electron-electron resonance (DEER) spectroscopy, can provide a high-resolution, long-range (~15-80 Å) probability distributions of distances between site-selected pairs of spin labels to resolve intra-protein distance parameters of unique protein conformations, as well as their respective likelihoods within a conformational ensemble. A current frontier in the field of DEER spectroscopy is utilizing this distance information in computational modeling to generate complete structural models of these multiple conformations. Although several methods have been developed for this purpose, modeling protein backbone structural rearrangements using multiple distance restraints remains challenging, due in part to the complexity provided by rotameric flexibility of the spin label side chain. Here, we overcome these challenges with ProGuide, a new framework for generating accurate structural models guided by DEER distance distribution information. Large conformational rearrangements are captured by performing iterative experimentally biased molecular dynamics simulations. In each iteration, spin-label rotameric heterogeneity is modeled using chiLife, and then changes are calculated to capture distance-probability density present in the experimental DEER distributions and lacking from the modeled one. The resulting models of this process then go through a selection to generate the ensemble that best recapitulates the DEER data. We illustrate the power of this method using published DEER data from a study of biased agonism in the angiotensin II type 1 receptor (AT1R), a prototypical G protein coupled receptor (GPCR). The resulting AT1R models consist of both Gq- and β-arrestin-biased conformations, including a completely novel β-arrestin-biased conformation. These models reveal structural insights involving tertiary structural rearrangements as well as residue-level changes in crucial microswitch motifs. Taken together, the results demonstrate the power and flexibility of ProGuide to investigate conformational rearrangements of large, complex proteins using DEER-derived distance restraints.
构象异质性是蛋白质功能不可或缺的一部分——从酶催化到信号转导——而可视化不同的构象状态需要能够提供此类结构信息的实验技术。一种特别强大的方法,双电子-电子共振(DEER)光谱,可以提供高分辨率、长程(约15-80 Å)的位点选择性自旋标记对之间距离的概率分布,以解析独特蛋白质构象的蛋白质内距离参数,以及它们在构象集合中的各自可能性。DEER光谱领域当前的一个前沿是在计算建模中利用这些距离信息来生成这些多种构象的完整结构模型。尽管为此已经开发了几种方法,但使用多个距离约束来模拟蛋白质主链结构重排仍然具有挑战性,部分原因是自旋标记侧链的旋转异构体灵活性带来的复杂性。在这里,我们使用ProGuide克服了这些挑战,ProGuide是一个由DEER距离分布信息指导生成准确结构模型的新框架。通过执行迭代的实验偏向分子动力学模拟来捕捉大的构象重排。在每次迭代中,使用chiLife对自旋标记旋转异构体的异质性进行建模,然后计算变化以捕捉实验DEER分布中存在而建模中缺乏的距离概率密度。该过程产生的模型随后经过选择以生成最能概括DEER数据的集合。我们使用来自一项关于血管紧张素II 1型受体(AT1R)(一种典型的G蛋白偶联受体(GPCR))偏向激动作用研究的已发表DEER数据来说明这种方法的强大之处。所得的AT1R模型包括Gq偏向和β-抑制蛋白偏向的构象,包括一种全新的β-抑制蛋白偏向构象。这些模型揭示了涉及三级结构重排以及关键微开关基序中残基水平变化的结构见解。综上所述,结果证明了ProGuide使用DEER衍生的距离约束来研究大型复杂蛋白质构象重排的能力和灵活性。