Li Da-Wei, Wang Cheng, Brüschweiler Rafael
Campus Chemical Instrument Center, The Ohio State University, Columbus, OH, 43210, USA.
Department of Chemistry and Biochemistry, The Ohio State University, CBEC Building, Columbus, OH, 43210, USA.
J Biomol NMR. 2017 Jul;68(3):195-202. doi: 10.1007/s10858-017-0119-4. Epub 2017 Jun 1.
Characterization of the chemical components of complex mixtures in solution is important in many areas of biochemistry and chemical biology, including metabolomics. The use of 2D NMR total correlation spectroscopy (TOCSY) experiments has proven very useful for the identification of known metabolites as well as for the characterization of metabolites that are unknown by taking advantage of the good resolution and high sensitivity of this homonuclear experiment. Due to the complexity of the resulting spectra, automation is critical to facilitate and speed-up their analysis and enable high-throughput applications. To better meet these emerging needs, an automated spin-system identification algorithm of TOCSY spectra is introduced that represents the cross-peaks and their connectivities as a mathematical graph, for which all subgraphs are determined that are maximal cliques. Each maximal clique can be assigned to an individual spin system thereby providing a robust deconvolution of the original spectrum for the easy extraction of critical spin system information. The approach is demonstrated for a complex metabolite mixture consisting of 20 compounds and for E. coli cell lysate.
溶液中复杂混合物化学成分的表征在生物化学和化学生物学的许多领域（包括代谢组学）都很重要。二维核磁共振全相关谱（TOCSY）实验已被证明对于鉴定已知代谢物以及利用该同核实验的高分辨率和高灵敏度来表征未知代谢物非常有用。由于所得光谱的复杂性，自动化对于促进和加速其分析以及实现高通量应用至关重要。为了更好地满足这些新出现的需求，引入了一种TOCSY光谱的自动自旋系统识别算法，该算法将交叉峰及其连接性表示为一个数学图，确定该图的所有子图为最大团。每个最大团都可以分配给一个单独的自旋系统，从而为原始光谱提供稳健的去卷积，以便轻松提取关键的自旋系统信息。该方法已在由20种化合物组成的复杂代谢物混合物和大肠杆菌细胞裂解物上得到验证。
