Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, China.
State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, 510070, China.
Comb Chem High Throughput Screen. 2023;26(9):1802-1811. doi: 10.2174/1386207325666220905155923.
Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throughput approach with the integration of cheminformatics and deep-learning methods to explore their mechanistic action, especially herb/drug-gene interaction networks.
Ten medicinal herbs for clinical osteoporosis treatment were selected. Chemical ingredients of the top 10 herbs were retrieved from the TCMIO database, and their predicted targets were obtained from the SEA server. Anti-osteoporosis clinical drugs and targets were collected from multidatabases. Chemical space, fingerprint similarity, and scaffold comparison of the compounds between herbs and clinical drugs were analyzed by RDKit and SKlearn. A network of herb-ingredient-target was constructed via Gephi, and GO and KEGG enrichment analyses were performed using clusterProfiler. Additionally, the bioactivity of compounds and targets was predicted by DeepScreening. Molecular docking of YYH flavonoids to HSD17B2 was accomplished by AutoDockTools.
Cheminformatics result depicts a pharmacological network consisting of 89 active components and 30 potential genes. The chemical structures of plant steroids, flavonoids, and alkaloids are key components for anti-osteoporosis effects. Moreover, bioinformatics result demonstrates that the active components of herbs mainly participate in steroid hormone biosynthesis and the TNF signaling pathway. Finally, deep-learning-based regression models were constructed to evaluate 22 anti-osteoporosis-related protein targets and predict the activity of 1350 chemical ingredients of the 10 herbs.
The combination of cheminformatics and deep-learning approaches sheds light on the exploration of medicinal herbs mechanisms, and the identification of novel and active compounds from medical herbs in complex molecular systems.
骨质疏松症是老年人中普遍存在的疾病。中药来源的天然化合物具有抗骨质疏松作用。由于化学成分和天然产物的复杂性，有必要开发一种高通量的方法，结合化学信息学和深度学习方法来探索其作用机制，特别是草药/药物-基因相互作用网络。
选择了十种治疗临床骨质疏松症的草药。从 TCMIO 数据库中检索了前 10 种草药的化学成分，并从 SEA 服务器中获得了它们的预测靶点。从多个数据库中收集了抗骨质疏松症的临床药物和靶点。通过 RDKit 和 SKlearn 分析了化合物之间的化学空间、指纹相似度和支架比较。通过 Gephi 构建了一个草药-成分-靶点网络，并使用 clusterProfiler 进行了 GO 和 KEGG 富集分析。此外，通过 DeepScreening 预测了化合物和靶点的生物活性。通过 AutoDockTools 完成了 YYH 类黄酮与 HSD17B2 的分子对接。
化学信息学结果描述了一个药理学网络，包含 89 个活性成分和 30 个潜在基因。植物甾体、黄酮类和生物碱的化学结构是抗骨质疏松作用的关键成分。此外，生物信息学结果表明，草药的活性成分主要参与甾体激素生物合成和 TNF 信号通路。最后，构建了基于深度学习的回归模型，以评估 22 个与抗骨质疏松相关的蛋白靶点，并预测 10 种草药的 1350 种化学成分的活性。
化学信息学和深度学习方法的结合为探索草药机制提供了思路，并从复杂的分子系统中识别出了新的和有活性的草药化合物。
