Comprehensive analysis of macromolecule structural variability in CryoEM/CryoET

项目摘要 这项提议旨在开发分析结构变异性的计算工具 低温电子显微镜(CryoEM)和低温电子断层成像技术对大分子的成像 (Cryoet)。因为大多数大分子的功能涉及它们自身之间的动态相互作用 成分或其他分子，这些大分子的结构灵活性往往是关键 完成它们的功能。CryoEM/CryoET制作玻璃化中嵌入的大分子的快照 ICE，它提供了不同组成和含量的单个蛋白质颗粒的直接信息 构象状态。使用先进的计算方法，我们将能够解决结构 了解蛋白质的异质性，并对其结构-功能关系有更深入的了解。这个 在这个方案中开发的算法将使用高斯混合模型来描述蛋白质结构 将蛋白质快照图像嵌入到潜在空间的表示和深度神经网络 描绘了它们的构象状态。在这个提案中，我们解决了蛋白质结构可变性的问题。 从三个方面。首先，我们将建立一条同时定位和构象精化的管道 对于单粒子分析，这将使解决具有大规模结构可变性的系统成为可能。 其次，我们将把来自分子模型的约束集成到我们的管道中，以便来自 生物化学可用于指导蛋白质的异质性分析。最后，我们将重点介绍Cryoet和 扩展该方法以研究细胞内大分子系统的动态。总括而言，建议的 这项工作将生产用于全面分析蛋白质结构变异性的软件工具，这将 为大分子的作用机制提供新的见解。

Project Summary This proposal aims to develop computational tools that analyze the structural variability of the macromolecules imaged by Cryogenic electron microscopy (CryoEM) and Cryogenic electron tomography (CryoET). As the function of most macromolecules involves dynamic interactions among their own components or with other molecules, the structural flexibility of those macromolecules is often key to accomplishing their functions. CryoEM/CryoET makes snapshots of macromolecules embedded in vitrified ice, which provides direct information of individual protein particles in different compositional and conformational states. Using advanced computational methods, we will be able to resolve the structural heterogeneity of proteins and gain a deeper understanding of their structure-function relationship. The algorithm developed in this proposal will be using the Gaussian mixture model for protein structure representation and deep neural network for embedding snapshot images of proteins onto a latent space depicting their conformational states. In this proposal, we address the issue of protein structural variability from three aspects. First, we will build a pipeline for simultaneous orientation and conformation refinement for single particle analysis, which will make it possible to solve systems with large-scale structural variability. Second, we will integrate constraints from molecular models into our pipeline, so that prior knowledge from biochemistry can be used to guide the protein heterogeneity analysis. Finally, we will focus on CryoET and expand the method to look into the dynamic of macromolecular systems inside cells. In sum, the proposed work will produce software tools for a comprehensive analysis of protein structural variability, which will provide new insights into the functioning mechanism of macromolecules.