Computational Tools for Protein Complex Structure Prediction from MS Data

根据 MS 数据预测蛋白质复杂结构的计算工具

• 批准号: 10441403
• 负责人: Steffen Lindert
• 金额: $ 11.67万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10441403
• 关键词: Address; Agreement; Algorithms; Appearance; Benchmarking; Binding; Burial; Communities; Complex; Computational Technique; Computing Methodologies; Cryoelectron Microscopy; Data; Data Analyses; Data Set; Deuterium; Development; Dissociation; Docking; Entropy; Exposure to; Fluorescence Resonance Energy Transfer; Gases; High Performance Computing; Hybrids; Hydrogen; Kinetics; Label; Lead; Ligand Binding; Lipids; Macromolecular Complexes; Mass Spectrum Analysis; Measurement; Measures; Membrane Proteins; Methods; Modeling; Molecular Conformation; Monitor; Ohio; Pattern; Phase; Procedures; Protein Complex Subunit; Protein Region; Proteins; Quaternary Protein Structure; RNA; Reaction; Research; Resolution; Resources; Rotation; Shapes; Structural Models; Structure; Surface; Techniques; Testing; Work; X-Ray Crystallography; base; biomacromolecule; complex data; computerized tools; experience; experimental study; flexibility; improved; interfacial; ion mobility; macromolecule; novel; protein complex; protein structure; protein structure prediction; restraint; stoichiometry; structural biology; supercomputer; tool

TR&D 5: Project Summary. The proposed Resource for Native Mass Spectrometry Guided Structural Biology aims to develop advanced MS techniques for the structural characterization of biomacromolecules such as protein:protein, membrane protein:lipid, and RNA:protein complexes. Experimental development in the resource will focus on effective separations methods to purify and deliver native proteins to the MS, effective surface induced dissociation methods for non-covalent interface cleavages and UVPD for covalent fragmentation of native protein complexes, and measurement of the intact complexes and dissociation products (subcomplexes and covalent fragments) with ion mobility MS (for conformations and conformational changes e.g., upon ligand binding) and/or high resolution MS. Valuable structural information about macromolecular complexes will be obtained. However, there is currently no automated way of generating structural restraints from the MS data, and those restraints are generally insufficient to generate high accuracy complex structures from the data alone. In TR&D 5, we are proposing that, in combination with novel computational methods, the restraints from SID and IM, combined with restraints from established methods such as hydrogen deuterium exchange (HDX) and covalent labeling (CL), are sufficient for improved macromolecular complex structure prediction. We will develop tools to automatically extract restraints from experimental MS data and incorporate them into the Rosetta structure prediction tools to guide protein complex structure prediction. The proposed research is structured into two main stages. Aim 1. We will develop computational tools for macromolecular complex structure prediction from solution measurements that are monitored by MS (H/D exchange and covalent labeling). We will implement quantitative covalent labeling and HDX exposure constraints into the Rosetta docking algorithm, such that it is driven by agreement with the exposure pattern of the docked subunits. This aim use complexes as testbeds or will be applied to predict structures from HDX and CL data for complexes from DBPs 1, 2, 3, 7 and 8 Aim 2. We will develop computational tools for macromolecular complex structure prediction from the surface- induced dissociation and collision cross sections from ion mobility experiments. We will develop new Rosetta docking scores that measure the agreement of complex models with the SID and IM CCS data. TR&D 5 is tightly integrated with the other TR&Ds because it aims to extend the applicability of the developed experimental methods by tailoring computational methods that allow structural modeling based on the experimental data. This aim will use SID onset energies, oligomeric products generated, and CCS values to test the procedure and to predict structures by using data from DBPs 1, 2, 3, 7 and 10.

TR＆D 5：项目摘要。天然质谱指导的结构生物学的拟议资源 旨在开发高级MS技术，用于生物大分子的结构表征，例如 蛋白质：蛋白质，膜蛋白：脂质和RNA：蛋白质复合物。资源的实验发展 将专注于有效的分离方法，以净化并将天然蛋白授予MS，有效表面 非共价界面切割和UVPD的诱导分离方法，用于共价碎片 天然蛋白质复合物以及完整复合物和解离产物的测量（亚复合物 带有离子迁移率MS的共价片段（用于构象和构象变化，例如配体 结合）和/或高分辨率MS。有关大分子复合物的宝贵结构信息将是 获得。但是，目前尚无自动化方法来从MS数据中生成结构性约束， 这些限制通常不足以从数据中产生高精度的复杂结构。 在TR＆D 5中，我们提出，结合新型计算方法，SID和 IM，结合已建立方法的约束，例如氢氘交换（HDX）和 共价标记（CL）足以改善大分子复杂结构的预测。我们将发展 从实验性MS数据中自动提取约束并将其纳入Rosetta的工具 指导蛋白质复杂结构预测的结构预测工具。拟议的研究结构为 两个主要阶段。 目的1。我们将开发用于从解决方案的大分子复杂结构预测的计算工具 通过MS（H/D交换和共价标记）监测的测量值。我们将实施定量 共价标签和HDX暴露限制到Rosetta对接算法，以使其由 与对接亚基的暴露模式一致。此目的使用复合物作为测试床或将是 应用于从HDX和CL数据中预测DBPS 1、2、3、7和8的复合物的结构 AIM 2。我们将开发从表面 - 大分子复杂结构预测的计算工具 从离子迁移率实验中诱导的解离和碰撞截面。我们将开发新的Rosetta 对接得分，该分数衡量了复杂模型与SID和IM CCS数据的一致性。 TR＆D 5紧密 与其他TR＆D集成在一起，因为它旨在扩展开发的实验的适用性 通过调整允许基于实验数据的结构建模的计算方法的方法。这 AIM将使用SID发作能量，生成的低聚产品和CCS值来测试该过程和 通过使用来自DBPS 1、2、3、7和10的数据来预测结构。