TitleComputation-Assisted Identification of Bioactive Compounds in Botanical Extracts: A Case Study of Anti-Inflammatory Natural Products from Hops.
Publication TypeJournal Article
Year of Publication2022
AuthorsBrown KS, Jamieson P, Wu W, Vaswani A, Magana AAlcazar, Choi J, Mattio LM, Cheong PHa-Yeon, Nelson D, Reardon PN, Miranda CL, Maier CS, Stevens JF
JournalAntioxidants (Basel)
Date Published2022 Jul 19

The slow pace of discovery of bioactive natural products can be attributed to the difficulty in rapidly identifying them in complex mixtures such as plant extracts. To overcome these hurdles, we explored the utility of two machine learning techniques, i.e., Elastic Net and Random Forests, for identifying the individual anti-inflammatory principle(s) of an extract of the inflorescences of the hops () containing hundreds of natural products. We fractionated a hop extract by column chromatography to obtain 40 impure fractions, determined their anti-inflammatory activity using a macrophage-based bioassay that measures inhibition of iNOS-mediated formation of nitric oxide, and characterized the chemical composition of the fractions by flow-injection HRAM mass spectrometry and LC-MS/MS. Among the top 10 predictors of bioactivity were prenylated flavonoids and humulones. The top Random Forests predictor of bioactivity, xanthohumol, was tested in pure form in the same bioassay to validate the predicted result (IC 7 µM). Other predictors of bioactivity were identified by spectral similarity with known hop natural products using the Global Natural Products Social Networking (GNPS) algorithm. Our machine learning approach demonstrated that individual bioactive natural products can be identified without the need for extensive and repetitive bioassay-guided fractionation of a plant extract.

Alternate JournalAntioxidants (Basel)
PubMed ID35883889
PubMed Central IDPMC9312012
Grant ListS10RR022589 / NH / NIH HHS / United States
S10RR027878 / NH / NIH HHS / United States
Buhler-Wang Research Fund / / OSU Foundation /