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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

基本信息

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
项目类别：
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

项目摘要

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.

研发成果5：项目总结。原生质谱引导结构生物学的建议资源