Using in vivo genetic and physical interaction data for structure determination of protein assemblies

项目总结 许多蛋白质通过形成大分子集合体发挥功能。描述这些组件的结构 在他们的蜂窝环境中仍然具有挑战性。传统的结构生物学方法可能会提供高度的 解析原子结构，但通常需要纯化的样品，可能只描述几个构象。我们 建议使用体内遗传相互作用和定量交联质谱(QXL-MS)的数据 建立蛋白质组件结构模型的实验，使科学界能够解决 目前传统的结构生物学方法无法解决的结构问题。例如，基因 点突变上位微阵列图谱(PE-MAP)平台互作图谱及深度突变扫描 (DMS)已经成为在其背景下以残基分辨率询问蛋白质的强大工具 生物学上相关的功能。同样，在活体QXL-MS方法非常适合于解剖物理 蛋白质之间的相互作用、全范围的结构动力学和残基的构象变化 决议。值得注意的是，体内的遗传相互作用和交联实验可以在不同的 确定蛋白质功能状态如何响应细胞环境变化的条件，这是一个问题 很难用其他方法接近。然而，活体内的遗传相互作用和交联数据集通常是 嘈杂、稀疏和模棱两可，这使得结构性解释具有挑战性。充分发挥互联网的潜力 活体遗传和物理相互作用数据，我们需要新的计算方法来最大化结构 从这些数据集中提取的信息。在这里，我们提出了一个全面的研究计划来开发 用于构建稳定和瞬时蛋白质组件的整合/杂交结构模型的工具。我们将重点关注(1) 开发贝叶斯评分函数，客观量化体内的噪音和歧义 实验数据，从而提高了模型的精确度和精度；(2)建立贝叶斯模型 分层模型来表示蛋白质集合，因此允许应用程序 构象和成分不同的系统；以及(3)创建验证工具以评估 使用活体数据获得的结构模型的精确度和准确性，因此允许明智地使用 模特们。最后，我们将与实验者密切合作，应用这些方法来确定 对传统结构生物学方法有所折射的蛋白质组件的结构，包括 痘苗病毒蛋白组件、与HIV-1衣壳结合的TRIM5α以及与以下相关的DDiS穿梭因子 蛋白酶体。总之，我们将通过增加投入的多样性来扩大结构生物学的范围 用于综合/混合结构建模的信息，从而允许生物的结构建模 不适用于传统结构生物学方法的系统。我们的方法将在 开源综合建模平台(IMP)软件，并为全球蛋白质数据库做出贡献 (WWPDB)努力验证、存档和传播综合结构。

PROJECT SUMMARY Many proteins function by forming macromolecular assemblies. Describing the structures of these assemblies in their cellular environment remains challenging. Traditional structural biology approaches may provide high- resolution atomic structures but usually require purified samples and might describe only a few conformers. We propose using data from in vivo genetic interaction and quantitative cross-linking mass-spectrometry (qXL-MS) experiments to build structural models of protein assemblies, empowering the scientific community to address structural questions that are currently out of reach of traditional structural biology methods. For example, genetic interaction mapping by point-mutant epistatic miniarray profile (pE-MAP) platform and deep mutational scanning (DMS) have emerged as powerful tools to interrogate proteins, at a residue resolution, in the context of their biologically relevant functions. Similarly, in vivo qXL-MS approaches are well-suited to dissect physical interactions between proteins, a full range of structural dynamics, and conformational changes at residue resolution. Notably, in vivo genetic interaction and cross-linking experiments can be performed under varying conditions to determine how protein functional states respond to changes in the cellular environment, a problem difficult to approach by other methods. However, in vivo genetic interaction and cross-linking datasets are usually noisy, sparse, and ambiguous, making structural interpretation challenging. To fully realize the potential of in vivo genetic and physical interaction data, we need new computational methods that maximize the structural information extracted from these datasets. Here, we propose a comprehensive research program to develop tools to build integrative/hybrid structure models of stable and transient protein assemblies. We will focus on (1) developing Bayesian scoring functions that objectively quantify the noise and ambiguity in the in vivo experimental data, therefore increasing the accuracy and precision of the models; (2) building Bayesian hierarchical models to represent the ensembles of protein assemblies, therefore allowing the application to conformational and compositionally heterogeneous systems; and (3) creating validation tools to assess the precision and accuracy of structural models obtained using in vivo data, therefore allowing judicious use of the models. Finally, in close collaboration with experimentalists, we will apply these methods to determine the structures of protein assemblies that have been refractive to traditional structural biology methods, including vaccinia virus protein assemblies, TRIM5α bound to the HIV-1 capsid, and Ddis shuttling factors associated with the proteasome. In conclusion, we will expand the scope of structural biology by increasing the variety of input information used for integrative/hybrid structure modeling and thus allow structural modeling of biological systems that are not amenable to traditional structural biology approaches. Our methods will be implemented in the open-source Integrative Modeling Platform (IMP) software and contribute to the worldwide Protein Data Bank (wwPDB) effort to validate, archive, and disseminate integrative structures.