Integrated NMR for Complex Systems

适用于复杂系统的集成 NMR

• 批准号: 10573330
• 负责人: Woonghee Lee
• 金额: $ 22.62万
• 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/10573330
• 关键词: 3-Dimensional; Acceleration; Algorithms; Automation; Bayesian Analysis; Benchmarking; 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; beta pleated sheet; computerized data processing; computing resources; conformer; cost; data acquisition; data handling; data integration; experimental study; graphical user interface; high dimensionality; improved; molecular dynamics; multidimensional data; multiple datasets; parallel computer; programs; 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总结