Chervin Justine, Stierhof Marc, Tong Ming Him, Peace Doe, Hansen Kine Østnes, Urgast Dagmar Solveig, Andersen Jeanette Hammer, Yu Yi, Ebel Rainer, Kyeremeh Kwaku, Paget Veronica, Cimpan Gabriela, Wyk Albert Van, Deng Hai, Jaspars Marcel, Tabudravu Jioji N
The Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen , Aberdeen AB24 3UE, Scotland, U.K.
Marbio, UiT The Arctic University of Norway, Breivika , N-9037, Tromsø, Norway.
J Nat Prod. 2017 May 26;80(5):1370-1377. doi: 10.1021/acs.jnatprod.6b01035. Epub 2017 Apr 26.
A new strategy for the identification of known compounds in Streptomyces extracts that can be applied in the discovery of natural products is presented. The strategy incorporates screening a database of 5555 natural products including 5098 structures from Streptomyces sp., using a high-throughput LCMS data processing algorithm that utilizes HRMS data and predicted LC retention times (t) as filters for rapid identification of known compounds in the natural product extract. The database, named StrepDB, contains for each compound the structure, molecular formula, molecular mass, and predicted LC retention time. All identified compounds are annotated and color coded for easier visualization. It is an indirect approach to quickly assess masses (which are not annotated) that may potentially lead to the discovery of new or novel structures. In addition, a spectral database named MbcDB was generated using the ACD/Spectrus DB Platform. MbcDB contains 665 natural products, each with structure, experimental HRESIMS, MS/MS, UV, and NMR spectra. StrepDB was used to screen a mutant Streptomyces albus extract, which led to the identification and isolation of two new compounds, legonmaleimides A and B, the structures of which were elucidated with the aid of MbcDB and spectroscopic techniques. The structures were confirmed by computer-assisted structure elucidation (CASE) methods using ACD/Structure Elucidator Suite. The developed methodology suggests a pipeline approach to the dereplication of extracts and discovery of novel natural products.
本文提出了一种用于鉴定链霉菌提取物中已知化合物的新策略，该策略可应用于天然产物的发现。该策略包括使用高通量液相色谱-质谱（LCMS）数据处理算法筛选一个包含5555种天然产物的数据库，其中包括来自链霉菌属的5098种结构，该算法利用高分辨率质谱（HRMS）数据和预测的液相色谱保留时间（t）作为过滤器，以快速鉴定天然产物提取物中的已知化合物。该数据库名为StrepDB，包含每种化合物的结构、分子式、分子量和预测的液相色谱保留时间。所有鉴定出的化合物都进行了注释并进行了颜色编码，以便于可视化。这是一种间接方法，用于快速评估可能潜在导致发现新结构或新颖结构的未注释质量。此外，使用ACD/Spectrus DB平台生成了一个名为MbcDB的光谱数据库。MbcDB包含665种天然产物，每种都有结构、实验性高分辨率电喷雾电离质谱（HRESIMS）、串联质谱（MS/MS）、紫外光谱（UV）和核磁共振光谱（NMR）。StrepDB用于筛选白色链霉菌突变体提取物，从而鉴定并分离出两种新化合物，即莱戈马来酰亚胺A和B，其结构借助MbcDB和光谱技术得以阐明。这些结构通过使用ACD/结构解析套件的计算机辅助结构解析（CASE）方法得到确认。所开发的方法为提取物的去重复和新天然产物的发现提出了一种流水线方法。
