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Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening

机译:核磁共振谱的自旋系统建模在代谢组学和小分子筛选中的应用

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摘要

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-1H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from .
机译:核磁共振(NMR)谱中异常丰富的信息内容通常用于鉴定和表征分子和分子相互作用,其用途广泛,包括临床生物标志物发现,药物发现,环境化学和代谢组学。来自参考NMR光谱的峰位置和强度的集合通常用作化合物的识别标记。通常,在特定的pH,温度和磁场强度条件下收集参考光谱,因为条件的变化会扭曲化合物的识别特征。参数化自旋之间的化学位移和偶联常数的自旋系统矩阵为化合物提供了比基于峰位置和强度的光谱特征更丰富的特征集。自旋系统矩阵将NMR谱库的适用性扩展到了收集数据的特定条件之外。除了能够在任何场强下模拟光谱外,还可以调整自旋参数以系统地探索由于其他实验条件(例如化合物浓度,pH或温度)变化而引起的化学位移模式的变化。我们提出了针对小分子的实验1D- 1 H NMR谱对自旋参数进行有效交互优化的方法和软件。我们已经使用该软件为一组关键的哺乳动物代谢物生成旋转系统矩阵,并且还使用该软件对用于基于NMR的配体筛选中的小分子的光谱进行参数化。该软件以及针对越来越多的化合物的优化的自旋系统矩阵数据可从处获得。

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