Comprehensive analysis of macromolecule structural variability in CryoEM/CryoET

项目摘要 该提案旨在开发计算工具，分析结构的变异性， 通过低温电子显微镜（CryoEM）和低温电子断层扫描成像的大分子 （CryoET）。由于大多数大分子的功能涉及它们自身之间的动态相互作用， 组分或与其他分子，这些大分子的结构灵活性往往是关键， 完成其功能。CryoEM/CryoET可对嵌入玻璃化细胞中的大分子进行快照 冰，它提供了不同组成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 研究蛋白质的异质性，并更深入地了解它们的结构-功能关系。的 在这个建议中开发的算法将使用高斯混合模型的蛋白质结构 用于将蛋白质的快照图像嵌入到潜在空间中的表示和深度神经网络 描绘它们的构象状态。在这个提议中，我们解决了蛋白质结构变异性的问题 从三个方面进行分析。首先，我们将建立一个管道，同时定向和构象细化 用于单粒子分析，这将使解决具有大规模结构可变性的系统成为可能。 其次，我们将把分子模型的约束整合到我们的管道中，这样， 生物化学可用于指导蛋白质异质性分析。最后，我们将重点介绍CryoET， 将该方法扩展到观察细胞内大分子系统的动力学。总而言之，拟议的 这项工作将产生用于蛋白质结构变异性综合分析的软件工具， 为大分子的功能机制提供了新的见解。

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.