Leggett Abigail, Wang Cheng, Li Da-Wei, Somogyi Arpad, Bruschweiler-Li Lei, Brüschweiler Rafael
Ohio State Biochemistry Program, The Ohio State University, Columbus, OH, United States.
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, United States.
Methods Enzymol. 2019;615:407-422. doi: 10.1016/bs.mie.2018.09.003. Epub 2018 Dec 7.
Metabolomics aims at the comprehensive identification of metabolites in complex mixtures to characterize the state of a biological system and elucidate their roles in biochemical pathways. For many biological samples, a large number of spectral features observed by NMR spectroscopy and mass spectrometry (MS) belong to unknowns, i.e., these features do not belong to metabolites that have been previously identified, and their spectral information is not available in databases. By combining NMR, MS, and combinatorial cheminformatics, the analysis of unknowns can be pursued in complex mixtures requiring minimal purification. This chapter describes the SUMMIT MS/NMR approach covering sample preparation, NMR and MS data collection and processing, and the identification of likely unknowns with the use of cheminformatics tools and the prediction of NMR spectral properties.
代谢组学旨在全面鉴定复杂混合物中的代谢物，以表征生物系统的状态并阐明它们在生化途径中的作用。对于许多生物样品，通过核磁共振光谱（NMR）和质谱（MS）观察到的大量光谱特征属于未知物，即这些特征不属于先前已鉴定的代谢物，并且它们的光谱信息在数据库中不可用。通过结合NMR、MS和组合化学信息学，可以在需要最少纯化的复杂混合物中对未知物进行分析。本章介绍了SUMMIT MS/NMR方法，涵盖样品制备、NMR和MS数据收集与处理，以及使用化学信息学工具鉴定可能的未知物和预测NMR光谱特性。
