Brown Kevin S, Jamieson Paige, Wu Wenbin, Vaswani Ashish, Alcazar Magana Armando, Choi Jaewoo, Mattio Luce M, Cheong Paul Ha-Yeon, Nelson Dylan, Reardon Patrick N, Miranda Cristobal L, Maier Claudia S, Stevens Jan F
Department of Pharmaceutical Sciences, Oregon State University, 1601 SW Jefferson Way, Corvallis, OR 97331, USA.
School of Chemical, Biological, and Environmental Engineering, Oregon State University, 116 Johnson Hall, 105 SW 26th Street, Corvallis, OR 97331, USA.
Antioxidants (Basel). 2022 Jul 19;11(7):1400. doi: 10.3390/antiox11071400.
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.
生物活性天然产物的发现速度缓慢，这可归因于在植物提取物等复杂混合物中快速鉴定它们存在困难。为克服这些障碍，我们探索了两种机器学习技术，即弹性网络和随机森林，用于鉴定啤酒花（）花序提取物中含有数百种天然产物的单一抗炎成分。我们通过柱色谱法对啤酒花提取物进行分离，得到40个不纯馏分，使用基于巨噬细胞的生物测定法测定它们的抗炎活性，该生物测定法测量对诱导型一氧化氮合酶介导的一氧化氮形成的抑制作用，并通过流动注射高分辨质谱和液相色谱 - 串联质谱对馏分的化学成分进行表征。生物活性的前10个预测因子包括异戊烯基黄酮和葎草酮。生物活性的顶级随机森林预测因子黄腐酚，在相同的生物测定中以纯形式进行测试以验证预测结果（IC 7 μM）。使用全球天然产物社会网络（GNPS）算法，通过与已知啤酒花天然产物的光谱相似性确定了其他生物活性预测因子。我们的机器学习方法表明，无需对植物提取物进行广泛且重复的生物测定指导分级分离，就可以鉴定出单一的生物活性天然产物。
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