Covariance-based NMR of Proteins and Complex Metabolite Mixtures

项目摘要 这项研究的总体目标是开发和应用新的核 磁共振(核磁共振)方法和基于网络的技术，用于快速和 大小核磁共振谱的可靠采集、处理和分析 生物分子。协方差核磁共振波谱原理，在 之前的资助期，将扩展到更高维度的光谱学， 包括GFT和投影重建核磁共振，以获得实质性的 沿间接维度的光谱分辨率的增加，并使 蛋白质主链和侧链共振的自动分配。 基于协方差的分辨率增强方法将被开发用于新的4D 增加短程和长程核磁共振距离的NOESY方案 确定所需的限制，包括特定于立体的分配 高质量的蛋白质结构。协方差核磁共振和其他基于矩阵的方法 对于复杂代谢物的分析，将开发混合物以获得 关于混合物成分的定量信息，这是其他方法无法获得的， 目标是实现自动化的高通量指纹识别和分析 代谢组学的新兴领域。这些方法将提供给广泛的 通过一套互连的Web服务器覆盖一系列用户。

Project Summary The overall objectives of this research are to develop and apply new nuclear magnetic resonance (NMR) methods and web-based technology for the rapid and reliable collection, processing, and analysis of NMR spectra of large and small biomolecules. The principles of Covariance NMR Spectroscopy, developed in the previous funding period, will be extended to higher dimensional spectroscopy, including GFT and projection reconstruction NMR, in order to gain substantial increase in spectral resolution along indirect dimensions and to enable the automated assignment of backbone and side-chain resonances of proteins. Covariance-based resolution enhancement methods will be developed for new 4D NOESY-schemes to increase the number of short- and long-range NMR distance constraints, including stereo-specific assignments, necessary for the determination of high-quality protein structures. Covariance NMR and other matrix-based methods for the analysis of complex metabolite mixtures will be developed to obtain quantitative information on the mixture components, not available by other methods, with the goal toward automated high-throughput fingerprinting and profiling in the emerging field of metabolomics. The methods will be made available to a broad range of users through a suite of interconnected web servers.