State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China.
State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China.
J Chromatogr A. 2023 Sep 13;1706:464243. doi: 10.1016/j.chroma.2023.464243. Epub 2023 Jul 28.
To accurately identify the metabolites is crucial in a number of research fields, and discovery of new compounds from the natural products can benefit the development of new drugs. However, the preferable phytochemistry or liquid chromatography/mass spectrometry approach is time-/labor-extensive or receives unconvincing identifications. Herein, we presented a strategy, by integrating offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), exclusion list-containing high-definition data-dependent acquisition (HDDDA-EL), and quantitative structure-retention relationship (QSRR) prediction of the retention time (t), to facilitate the in-depth and more reliable identification of herbal components and thus to discover new compounds more efficiently. Using the saponins in Panax quinquefolius flower (PQF) as a case, high orthogonality (0.79) in separating ginsenosides was enabled by configuring the XBridge Amide and CSH C18 columns. HDDDA-EL could improve the coverage in MS acquisition by 2.26 folds compared with HDDDA (2933 VS 1298). Utilizing 106 reference compounds, an accurate QSRR prediction model (R = 0.9985 for the training set and R = 0.88 for the validation set) was developed based on Gradient Boosting Machine (GBM), by which the predicted t matching could significantly reduce the isomeric candidates identification for unknown ginsenosides. Isolation and establishment of the structures of two malonylginsenosides by NMR partially verified the practicability of the integral strategy. By these efforts, 421 ginsenosides were identified or tentatively characterized, and 284 thereof were not ever reported from the Panax species. The current strategy is thus powerful in the comprehensive metabolites characterization and rapid discovery of new compounds from the natural products.
准确鉴定代谢物在许多研究领域至关重要，从天然产物中发现新化合物可以促进新药的开发。然而，首选的植物化学或液相色谱/质谱方法耗时耗力，或者鉴定结果不可信。在此，我们提出了一种策略，通过整合离线二维液相色谱/离子淌度-四极杆飞行时间质谱（2D-LC/IM-QTOF-MS）、包含排除列表的高清晰度数据依赖采集（HDDDA-EL）以及保留时间（t）的定量结构保留关系（QSRR）预测，以促进草药成分的深入和更可靠的鉴定，从而更有效地发现新化合物。以西洋参花（PQF）中的皂苷为例，通过配置 XBridge Amide 和 CSH C18 柱，实现了高达 0.79 的高分离度。与 HDDDA 相比，HDDDA-EL 可以将 MS 采集的覆盖率提高 2.26 倍（2933 对 1298）。利用 106 个参考化合物，通过梯度提升机（GBM）建立了一个准确的 QSRR 预测模型（训练集的 R=0.9985，验证集的 R=0.88），通过该模型对未知人参皂苷的预测 t 值匹配可以显著减少对同分异构体候选物的鉴定。通过 NMR 对两种丙二酰基人参皂苷的分离和结构建立，部分验证了整体策略的实用性。通过这些努力，鉴定或暂定鉴定了 421 个人参皂苷，其中 284 种从未在人参属中报道过。因此，该策略在天然产物中具有强大的综合代谢物特征描述和新化合物快速发现能力。
