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

使用体内遗传和物理相互作用数据确定蛋白质组装体的结构

• 批准号: 10714613
• 负责人: Ignacia Echeverria Riesco
• 金额: $ 40.38万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-08 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10714613
• 关键词: Address; Archives; Bayesian Modeling; Binding; Capsid; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Environment; Genetic; Genotype; HIV-1; Hybrids; Maps; Mass Spectrum Analysis; Methods; Modeling; Molecular Conformation; Noise; Phenotype; Proteins; Research; Resolution; Sampling; Structural Models; Structure; System; Vaccinia virus; Validation; Viral Proteins; biological systems; conformer; crosslink; empowerment; experimental study; in vivo; macromolecular assembly; multicatalytic endopeptidase complex; mutant; mutation screening; open source; programs; protein data bank; protein function; protein structure; structural biology; tool; Address; Archives; Bayesian Modeling; Binding; Capsid; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Environment; Genetic; Genotype; HIV-1; Hybrids; Maps; Mass Spectrum Analysis; Methods; Modeling; Molecular Conformation; Noise; Phenotype; Proteins; Research; Resolution; Sampling; Structural Models; Structure; System; Vaccinia virus; Validation; Viral Proteins; biological systems; conformer; crosslink; empowerment; experimental study; in vivo; macromolecular assembly; multicatalytic endopeptidase complex; mutant; mutation screening; open source; programs; protein data bank; protein function; protein structure; structural biology; tool

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.

项目摘要 许多蛋白质通过形成大分子组件来发挥作用。描述这些组件的结构 在他们的细胞环境中，仍然受到挑战。传统的结构生物学方法可能会提供高 分辨率原子结构，但通常需要纯化的样品，并且可能只描述了几个构象异构体。我们 使用来自体内遗传相互作用和定量交联质谱法（QXL-MS）的数据的提案 实验以建立蛋白质组件的结构模型，使科学界能够解决 当前无法触及的传统结构生物学方法的结构性问题。例如，通用 通过点突变的上皮Miniarray轮廓（PE-MAP）平台和深突变扫描的互动映射 （DMS）已成为以退休解决的强大工具来审问蛋白质 生物学相关的功能。同样，体内QXL-MS方法非常适合剖析物理 蛋白质之间的相互作用，一系列结构动力学和退休时构象变化 解决。值得注意的是，可以在变化下进行体内遗传相互作用和交联实验 确定蛋白质功能状态如何应对细胞环境变化的条件，一个问题 难以通过其他方法处理。但是，体内遗传相互作用和交联数据集通常是 嘈杂，稀疏和模棱两可，引起结构性解释挑战。充分意识到 体内遗传和物理交互数据，我们需要新的计算方法，以最大化结构 从这些数据集中提取的信息。在这里，我们提出了一项全面的研究计划，以开发 建立稳定和瞬态蛋白质组件的集成/混合结构模型的工具。我们将专注于（1） 开发贝叶斯评分功能，可以客观地量化体内的噪声和歧义 实验数据，因此提高了模型的准确性和精度； （2）建造贝叶斯 层次模型表示蛋白质组件的组合，因此允许应用程序 构象和完全异质系统； （3）创建验证工具来评估 使用体内数据获得的结构模型的精确和准确性，因此可以明智地使用 型号。最后，通过与实验者密切合作，我们将应用这些方法来确定 已经屈光于传统结构生物学方法的蛋白质组件的结构，包括 离发病毒蛋白质组件，TRIM5α与HIV-1帽结合，DDIS穿梭因子与 蛋白酶体。总之，我们将通过增加输入的种类来扩大结构生物学的范围 用于集成/混合结构建模的信息，从而允许生物学的结构建模 不适合传统结构生物学方法的系统。我们的方法将在 开源集成建模平台（IMP）软件，并为全球蛋白质数据库做出了贡献 （WWPDB）努力验证，存档和传播集成结构。