Qiu Feng, McAlpine James B, Lankin David C, Burton Ian, Karakach Tobias, Chen Shao-Nong, Pauli Guido F
Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago , Chicago, Illinois 60612, United States.
Anal Chem. 2014 Apr 15;86(8):3964-72. doi: 10.1021/ac500188j. Epub 2014 Mar 27.
The interpretation of NMR spectroscopic information for structure elucidation involves decoding of complex resonance patterns that contain valuable molecular information (δ and J), which is not readily accessible otherwise. We introduce a new concept of 2D-NMR barcoding that uses clusters of fingerprint signals and their spatial relationships in the δ-δ coordinate space to facilitate the chemical identification of complex mixtures. Similar to widely used general barcoding technology, the structural information of individual compounds is encoded as a specifics pattern of their C,H correlation signals. Software-based recognition of these patterns enables the structural identification of the compounds and their discrimination in mixtures. Using the triterpenes from various Actaea (syn. Cimicifuga) species as a test case, heteronuclear multiple-bond correlation (HMBC) barcodes were generated on the basis of their structural subtypes from a statistical investigation of their δH and δC data in the literature. These reference barcodes allowed in silico identification of known triterpenes in enriched fractions obtained from an extract of A. racemosa (black cohosh). After dereplication, a differential analysis of heteronuclear single-quantum correlation (HSQC) spectra even allowed for the discovery of a new triterpene. The 2D barcoding concept has potential application in a natural product discovery project, allowing for the rapid dereplication of known compounds and as a tool in the search for structural novelty within compound classes with established barcodes.
用于结构解析的核磁共振波谱信息解读，涉及对包含有价值分子信息（δ和J）的复杂共振模式进行解码，而这些信息用其他方法则难以轻易获取。我们引入了二维核磁共振条形码这一新概念，它利用指纹信号簇及其在δ-δ坐标空间中的空间关系，来促进对复杂混合物的化学鉴定。与广泛使用的通用条形码技术类似，单个化合物的结构信息被编码为其碳氢相关信号的特定模式。基于软件对这些模式的识别，能够实现化合物的结构鉴定及其在混合物中的区分。以来自不同类叶升麻属（同义词：升麻属）物种的三萜类化合物作为测试案例，通过对文献中它们的δH和δC数据进行统计研究，根据其结构亚型生成了异核多键相关（HMBC）条形码。这些参考条形码使得能够在计算机上鉴定从总状升麻（黑升麻）提取物中获得的富集馏分中的已知三萜类化合物。在去重复之后，对异核单量子相关（HSQC）谱的差异分析甚至还发现了一种新的三萜类化合物。二维条形码概念在天然产物发现项目中具有潜在应用，既能够实现已知化合物的快速去重复，又可作为在具有既定条形码的化合物类别中寻找结构新颖性的工具。
