Integrated NMR for Complex Systems

适用于复杂系统的集成 NMR

基本信息

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
项目类别：
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

项目摘要

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谱中的每个共振分配给分子中的个体原子是科学的几乎每个人 NMR项目。用于较大的蛋白质和复合物，不对称的低聚物以及具有主要次级次级的系统螺旋膜蛋白或β-纤维纤维的结构。样品制备，实验选择，数据处理，信号识别和数据分析以实现任务 - 费用，耗时，需要大量的手动干预需要专业知识做得好在这里，我们建议开发算法和指导用户界面，以自动化更高的处理尺寸光谱，改善自动峰采摘以及量身定制的NMR光谱以进行分配最大化数据并最大程度地减少全自动，集成的数据采集，分配和结构的成本确定平台。减少NMR研究的时间和成本，使NMR更容易被广泛的研究社区访问。