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Natural product discovery is hindered by the lack of tools that integrate untargeted nuclear magnetic resonance and mass spectrometry data on a library scale. This article describes the first application of the innovative NMR/MS-based machine learning tool, the "Structure-Oriented Fractions Screening Platform (SFSP)", enabling functional-group-guided fractionation and accelerating the discovery and characterization of undescribed natural products. The concept was applied to the extract of a marine fungus known to be a prolific producer of diverse natural products. With the assistance of SFSP, we isolated 24 flavipidin derivatives and five phenalenone analogues from Aspergillus sp. GE2-6, revealing 27 undescribed compounds. Compounds 7–22 were proposed as isomeric derivatives featuring a 5/6-ring fusion, formed by the dimerization of flavipidin E (5). Compounds 23 and 24 were envisaged as isomeric derivatives with a 6/5/6-ring fusion, generated through the degradation of two flavipidin E molecules. Furthermore, flavipidin A (1) and asperphenalenone E (28) exhibited potent anti-influenza (PR8) activities, with IC50 values of 21.9 ± 0.2 and 12.9 ± 0.1 μM, respectively. Meanwhile, asperphenalenone (26) and asperphenalenone P (27) treatments exhibited significant inhibition of HIV pseudovirus infection in 293FT cells, boasting IC50 values of 6.1 ± 0.9 and 4.6 ± 1.1 μM, respectively. Overall, SFSP streamlines natural product isolation through NMR and MS data integration, as showcased by the discovery of numerous undescribed flavipidins and phenalenones based on NMR olefinic signals and low-field hydroxy signals.
Ge et al. (Mon,) studied this question.