Chan Ki, Leung Henry Chi Ming, Tsoi James Kit-Hon
1Dental Materials Science, Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR PRC.
2Department of Computer Science, Faculty of Engineering, University of Hong Kong, Pokfulam, Hong Kong SAR PRC.
Chin Med. 2020 Mar 31;15:31. doi: 10.1186/s13020-020-00313-1. eCollection 2020.
Flavonoids in Chinese Medicine have been proven in animal studies that could aid in osteogenesis and bone formation. However, there is no consented mechanism for how these phytochemicals action on the bone-forming osteoblasts, and henceforth the prediction model of chemical screening for this specific biochemical function has not been established. The purpose of this study was to develop a novel selection and effective approach of flavonoids on the prediction of bone-forming ability via osteoblastic voltage-gated calcium (CaV) activation and inhibition using molecular modelling technique.
Quantitative structure-activity relationship (QSAR) in supervised maching-learning approach is applied in this study to predict the behavioral manifestations of flavonoids in the CaV channels, and developing statistical correlation between the biochemical features and the behavioral manifestations of 24 compounds (Training set: Kaempferol, Taxifolin, Daidzein, Morin, Scutellarein, Quercetin, Apigenin, Myricetin, Tamarixetin, Rutin, Genistein, 5,7,2'-Trihydroxyflavone, Baicalein, Luteolin, Galangin, Chrysin, Isorhamnetin, Naringin, 3-Methyl galangin, Resokaempferol; test set: 5-Hydroxyflavone, 3,6,4'-Trihydroxyflavone, 3,4'-Dihydroxyflavone and Naringenin). Based on statistical algorithm, QSAR provides a reasonable basis for establishing a predictive correlation model by a variety of molecular descriptors that are able to identify as well as analyse the biochemical features of flavonoids that engaged in activating or inhibiting the CaV channels for osteoblasts.
The model has shown these flavonoids have high activating effects on CaV channel for osteogenesis. In addition, scutellarein was ranked the highest among the screened flavonoids, and other lower ranked compounds, such as daidzein, quercetin, genistein and naringin, have shown the same descending order as previous animal studies.
This predictive modelling study has confirmed and validated the biochemical activity of the flavonoids in the osteoblastic CaV activation.
中药中的黄酮类化合物在动物研究中已被证明有助于成骨和骨形成。然而，这些植物化学物质如何作用于成骨的成骨细胞，目前尚无公认的机制，因此尚未建立针对这种特定生化功能的化学筛选预测模型。本研究的目的是通过分子建模技术，开发一种基于成骨细胞电压门控钙（CaV）激活和抑制来预测黄酮类化合物成骨能力的新型有效筛选方法。
本研究采用监督式机器学习方法中的定量构效关系（QSAR）来预测黄酮类化合物在CaV通道中的行为表现，并建立24种化合物（训练集：山奈酚、紫杉叶素、大豆苷元、桑色素、黄芩素、槲皮素、芹菜素、杨梅素、异鼠李素、芦丁、染料木素、5,7,2'-三羟基黄酮、黄芩苷、木犀草素、高良姜素、 Chrysin、异鼠李素、柚皮苷、3-甲基高良姜素、Resokaempferol；测试集：5-羟基黄酮、3,6,4'-三羟基黄酮、3,4'-二羟基黄酮和柚皮素）的生化特征与行为表现之间的统计相关性。基于统计算法，QSAR通过各种分子描述符为建立预测相关模型提供了合理依据，这些描述符能够识别和分析参与激活或抑制成骨细胞CaV通道的黄酮类化合物的生化特征。
该模型显示这些黄酮类化合物对成骨的CaV通道具有高激活作用。此外，黄芩素在筛选出的黄酮类化合物中排名最高，其他排名较低的化合物，如大豆苷元、槲皮素、染料木素和柚皮苷，显示出与先前动物研究相同的降序排列。
这项预测建模研究证实并验证了黄酮类化合物在成骨细胞CaV激活中的生化活性。
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