Dynamics of Protein Assemblies by Analytical Ultracentrifugation

分析超速离心的蛋白质组装动力学

• 批准号: 10262996
• 负责人: PETER SCHUCK
• 金额: $ 31.9万
• 依托单位: NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10262996
• 关键词: 3D Print; Behavior; Binding; Bovine Serum Albumin; Cells; Chemicals; Collaborations; Complex; Computer software; Custom; Data; Data Analyses; Detection; Environment; Equilibrium; Exhibits; Fluorescence; France; Goals; Hypergravity; Knowledge; Life; Liquid substance; Macromolecular Complexes; Measures; Mechanics; Modeling; Multiprotein Complexes; Optics; Pattern; Phase Transition; Plant Roots; Protein Dynamics; Proteins; Reaction; Refractive Indices; Sampling; Sedimentation process; Serum; Signal Transduction; Stratification; System; Techniques; Time; Vacuum; Work; analytical ultracentrifugation; base; design; dynamic system; experimental study; improved; indexing; lens; macromolecular assembly; mathematical model; novel strategies; particle; performance tests; seal; sedimentation coefficient; sedimentation velocity; signal processing; technique development; theories

One major thrust of our work has been the continued improvement of nonideal sedimentation velocity (nonideal SV) as a technique for measuring macromolecular size-distributions in highly concentrated solutions. The goal is to study proteins at concentrations closer to the intracellular environment, where weak interactions can govern a wide spectrum of behavior, including dynamic multi-protein complex formation and liquid-liquid phase transition. Our recent breakthrough in the analysis of polydisperse concentrated protein solutions came from the introduction of a mean-field approach to account for hydrodynamic interactions in the sedimentation particle mixtures. It is based on nonideality coefficients that arise from first-order approximations of concentration-dependent behavior, which, unfortunately, are limited to solutions with macromolecular volume occupancy below 5%. To remove this limitation, we have implemented higher-order approximations rooted in statistical fluid mechanics. In the past year, we have collected experimental data of sedimentation boundaries of bovine serum albumin at up to 150 mg/mL, and of undiluted serum. First results validate the sedimentation model. An experimental problem when working at high concentration is presented by optical aberrations in the strong refractive index gradients of the sedimentation boundary. This lensing effect can be minimized by short optical pathlengths, and therefore we have further improved the design of our 3D printed sample holders. We believe we have reached limits imposed by the need to seal the sample against high vacuum, achieve stability in high gravity, and the ability to create access ports for sample filling. We turned to a different approach to allow studying still higher protein concentrations: we have devised a technique for experimentally measuring the magnitude of optical aberrations, and developed a mathematical model of the optical distortions that we embedded it into our sedimentation data analysis. We have begun testing the performance of these approaches. A second major effort in our work on SV was directed at making sedimentation analysis more information-rich for the study of multi-protein interactions. We have previously pursued this goal by multi-signal approaches and creating temporal signals by photoswitching. However, another opportunity resides in the stratification of solution during the sedimentation process: Sedimenting systems of dynamically interacting proteins with complex life-times shorter than 1,000 sec exhibit richly patterned sedimentation profiles, with sedimentation boundaries that depend in complex ways on protein concentrations, equilibrium constants, and sedimentation coefficients associated with the different assembly states. We have previously developed effective particle theory that provides a physical explanation for sedimentation boundary patterns. In the current year we have integrated this theory into a new approach for interpreting SV data from sedimenting interacting systems. It allows taking advantage of the entire sedimentation pattern, rather than only the average sedimentation velocity, and thereby enhances the information content of SV experiments. We have implemented this into our data analysis software with a user interface that allows creation of customized binding models. Finally, we have continued to develop a fluorescence detection system for analytical ultracentrifugation, in collaboration with John Kakareka and Thomas Pohida (CIT). Both improvements in the optical setup as well as signal processing of index cells were achieved. To disseminate knowledge of analytical ultracentrifugation we have made this technique a major focus in our FEBS Practical Course that was held in January 2020 in Grenoble, France.

我们工作的一个主要目的是，非理想沉积速度（非理想的SV）作为一种测量高度浓缩溶液中大分子尺寸分布的技术。 目的是以更接近细胞内环境的浓度研究蛋白质，在这种浓度上，弱相互作用可以控制各种行为，包括动态多蛋白质复合物的形成和液体液相变。 在分析多分散浓缩蛋白溶液中，我们最近的突破来自引入平均场方法，以说明沉积颗粒混合物中的流体动力相互作用。 它基于非理想系数，是由浓度依赖性行为的一阶近似值产生的，不幸的是，这仅限于大分子体积占用率低于5％的溶液。 为了消除此限制，我们已经实施了植根于统计流体力学的高阶近似值。在过去的一年中，我们收集了牛血清白蛋白沉积边界的实验数据，最多可达150 mg/ml和未稀释的血清。首先结果验证沉积模型。 在高浓度下工作时的一个实验问题是通过在沉积边界的强折射率梯度中的光学畸变提出的。 可以通过简短的光路长度最小化这种镜头效果，因此我们进一步改进了3D打印样品持有人的设计。我们认为，我们已经达到了将样品密封在高真空中，在高重力中实现稳定性以及创建用于样品填充的访问端口的能力所施加的限制。 我们转向了一种不同的方法来允许研究更高的蛋白质浓度：我们设计了一种实验测量光学畸变幅度的技术，并开发了光学畸变的数学模型，我们将其嵌入了沉积数据分析中。我们已经开始测试这些方法的性能。 在我们的SV工作中，第二次重大努力是为了使沉积分析更多的信息丰富，以研究多蛋白质相互作用。 我们以前曾通过多信号方法追求这一目标，并通过拍摄照片来创建时间信号。 然而，在沉积过程中解决方案分层的另一个机会是：动态相互作用的蛋白质的沉积系统，寿命复杂的时间短，短于1,000秒的蛋白质表现出丰富的图案沉积剖面，其沉积边界具有复杂的蛋白质浓度，平衡常数，平衡常数和与不同组件相关的蛋白质浓度。我们以前已经开发了有效的粒子理论，该理论为沉降边界模式提供了物理解释。在当年，我们将这一理论纳入了一种新方法，用于解释沉积系统中的SV数据。 它允许利用整个沉积模式，而不仅仅是平均沉积速度，从而增强了SV实验的信息含量。 我们已使用用户界面将其实施到我们的数据分析软件中，该软件允许创建自定义的绑定模型。 最后，我们继续与John Kakareka和Thomas Pohida（CIT）合作开发了用于分析超速离心的荧光检测系统。 光学设置的两种改进以及指数细胞的信号处理。 为了传播分析性超速离心的知识，我们使这项技术成为了2020年1月在法国格勒诺布尔举行的FEB​​S实践课程中的主要重点。