Integrated NMR for Complex Systems

适用于复杂系统的集成 NMR

• 批准号: 10089604
• 负责人: Woonghee Lee
• 金额: $ 24.61万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-02 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10089604
• 关键词: 3-Dimensional; Algorithms; Automation; Bayesian Analysis; Benchmarking; Books; Chemicals; Communities; Complex; Consumption; Cryoelectron Microscopy; Data; Data Analyses; Data Collection; Data Set; Detection; Dimensions; Disease; Feedback; Homology Modeling; Hybrids; Individual; Infrastructure; Intervention; Joints; Label; Machine Learning; Manuals; Membrane Proteins; Methods; Modernization; Morphologic artifacts; Noise; Performance; Phase; Preparation; Process; Proteins; Reproducibility; Research; Resolution; Resources; Roentgen Rays; Sampling; Signal Transduction; Source; Speed; Structural Models; Structure; System; Technology; Testing; Time; Titrations; United States National Institutes of Health; Vertebral column; Visualization; Visualization software; base; beta pleated sheet; computerized data processing; computing resources; conformer; cost; data acquisition; data handling; experimental study; graphical user interface; high dimensionality; improved; molecular dynamics; multidimensional data; multiple datasets; parallel computer; programs; protein complex; reconstruction; restraint; solid state nuclear magnetic resonance; three dimensional structure; tool

TR&D 3 SUMMARY Assigning each resonance in an NMR spectrum to individual atoms in a molecule is essential to almost every NMR project. Automated programs are successful for assignment of smaller soluble proteins, but frequently fail for larger proteins and complexes, asymmetric oligomers, and systems with predominantly one secondary structure such as helical membrane proteins or beta-sheet fibrils. For most systems, the assignment process – sample preparation, experiment selection, data processing, signal identification and data analysis to achieve assignments – is expensive, time consuming and requires significant manual effort. This manual intervention requires expertise to be done well, and poor execution at any step makes subsequent steps more difficult. Here we propose to develop algorithms and guided user interfaces that will automate processing of higher dimensional spectra, improve automated peak picking, and tailor acquisition of NMR spectra for assignment to maximize data and minimize cost in a fully-automated, integrated data acquisition, assignment and structure determination platform. Automation of this process will improve the reproducibility of NMR data analysis, reduce the time and cost of NMR studies, and make NMR more accessible to the broad research community.

TR&D 3摘要 将NMR光谱中的每个共振与分子中的单个原子联系起来， 核磁共振项目。自动化程序对于较小的可溶性蛋白质的分配是成功的，但经常失败 对于较大的蛋白质和复合物、不对称寡聚体和主要具有一个二级结构的系统， 结构如螺旋膜蛋白或β-折叠原纤维。对于大多数系统，分配过程- 实现样品制备、实验选择、数据处理、信号识别和数据分析 作业-是昂贵的、耗时的并且需要大量的手动工作。这种人工干预 需要专业知识才能做好，任何一步的执行不力都会使后续步骤更加困难。 在这里，我们建议开发算法和指导用户界面，将自动处理更高的 三维光谱，改进自动峰拾取，并定制NMR光谱的采集，以分配给 在全自动、集成的数据采集、分配和结构中最大限度地利用数据并最大限度地降低成本 决心平台该过程的自动化将提高NMR数据分析的再现性， 减少核磁共振研究的时间和成本，使核磁共振更容易为广大的研究社区所用。