关键词: 结构相似比对, 网络药理学, 蜂胶, 代谢性疾病, 黄酮类, 酚酸及酯类, 预测成功率

Abstract: Chronic metabolic diseases, such as type 2 diabetes and obesity, have brought a huge burden to our society. Certain functional foods could bring health benefits to people with metabolic abnormalities, and sometimes help delay the onset of metabolic diseases. Propolis is a traditional Chinese medicine, and has been used as a raw material of functional foods for quite a long time. Pharmacological studies and clinical trials have provided evidence that propolis and its active components could be promising candidates for improving metabolic health. In order to develop standardized functional foods with consistent quality and functions, a systemic view of the mechanism of action is required. We should also be aware that propolis is a combination of multiple active components. Network pharmacology is a recently developed method as an integrative system which enables a systemic investigation of interactions between multiple components and multiple targets. The method of network pharmacology has been used in studies of traditional Chinese medicine. In order to survey the molecular mechanisms of propolis components in treating metabolic diseases, we use structural similarity search to predict therapeutic targets of propolis flavonoids, phenolic acids and esters, and to construct a ‘component-target-disease’ network. Comparing with published data, we calculated that the success ratio of prediction was 86.2%. Based on the network, we concluded that PPARγ, ESR1, ESR2, SITR1 and PTPN1 are key targets of propolis. PPARγ is the most important target for both flavonoids and phenolic acids. Propolis flavonoids and some phenolic acids and esters contribute to the regulation of glucose and lipid metabolism by propolis through various pathways such as lipid metabolism, adipocyte differentiation, insulin secretion and insulin resistance. Based on the results of our study, we introduced a new research tool that can be used in the early stage of functional food development. We found that network pharmacology could provide information for formulating product standards for functional foods, which is important for innovation and upgrading of food industry.

Key words: structural similarity search, network pharmacology, propolis, metabolic diseases, flavonoids, phenolic acids and esters, prediction ratio