基于计算机分子模拟技术从天然产物中筛选PPARγ激动剂

doi:10.3760/cma.j.issn.1007-1245.2022.09.005

国际医药卫生导报 ›› 2022, Vol. 28 ›› Issue (9): 1204-1208.DOI: 10.3760/cma.j.issn.1007-1245.2022.09.005

基于计算机分子模拟技术从天然产物中筛选PPARγ激动剂

刘治国¹ 崔文强² 魏叶³ 朱心瑶³ 杨雪¹

¹南京医科大学附属宿迁第一人民医院宿迁市第一人民医院，宿迁　223800；
²东北农业大学，哈尔滨　150030；
³徐州医科大学，徐州　221004

收稿日期:2022-02-15 出版日期:2022-05-01 发布日期:2022-05-11
通讯作者: 杨雪，Email：xyangsq@163.com
基金资助:
江苏省“六大人才高峰”项目（WSN-342）；

江苏省青年医学重点人才项目（QNRC2016480）；

宿迁市科技计划项目（S201720）

Screening of PPARγ agonists from natural products based on computer molecular modeling

Liu Zhiguo¹, Cui Wenqiang², Wei Ye³, Zhu Xinyao³, Yang Xue¹

¹Suqian First People's Hospital Affiliated to Nanjing Medical University, First People's Hospital of Suqian City, Suqian 223800, China;
²Northeast Agricultural University, Harbin 150030, China;
³Xuzhou Medical University, Xuzhou 221004, China

Received:2022-02-15 Online:2022-05-01 Published:2022-05-11
Contact: Yang Xue, Email: xyangsq@163.com
Supported by:
Project of Six Talent Peaks in Jiangsu (WSN-342);

Project for Key Young Medical Talents in Jiangsu (QNRC2016480);

Project of Plan of Science and Technology in Suqian (S201720)

摘要/Abstract

摘要： 目的　采用分子对接技术从天然产物中筛选PPARγ激动剂。方法　研究时间为2021年1月至9月。基于过氧化物酶体增殖物激活受体γ（PPARγ）的PDB晶型结构（PDB Code：6md4），构建虚拟靶标模型，以天然产物数据库中的1 680个化合物为配体筛选对象，以6md4原配体分子为对照。首先采用SYBYL软件对天然小分子化合物进行虚拟筛选，随后对排名前20位的化合物进行高精度分子对接，最后再使用PyMol软件分析结合稳定的化合物。结果　根据评分结果并结合冲突、极性和相似度，最终筛选出橙皮苷等20个小分子PPARγ激动剂，其中橙皮苷能够与PPARγSer289、His323、Tyr327等多个氨基酸形成氢键，结合良好。结论　从天然产物中筛选出潜在的PPARγ激动剂，为发现新型抗糖尿病的先导化合物或膳食补充剂提供基础。

关键词: 2型糖尿病, PPARγ, 计算机虚拟筛选, 分子对接

Abstract: Objective　To screen PPARγ agonists from natural products by molecular docking. Methods　This study was from January to December 2021. Based on the crystal structure of peroxisome proliferator-activated receptor gamma (PPARγ) (PDB Code: 6md4), a virtual target model was constructed. A total of 1 680 compounds in the natural product database were used as the ligands for screening, and the 6md4 pro-ligands were used as control. Firstly, the SYBYL software was used for the virtual screening of natural small molecular compounds, followed by high-precision molecular docking of the top 20 compounds, and finally the PyMol software was used to analyze the stable binding compounds. Results　According to the scoring results, crash, polar, 20 small-molecule PPAR gamma agonists, such as hesperidin, and the like, were finally screened out. Among them, hesperidin could form hydrogen bonds with many amino acids, such as PPARγSer289, His323, Tyr327, and so on. Conclusion　The potential PPARγ agonists were screened from natural products so as to provide basis for the discovery of new anti-diabetic lead compounds or dietary supplements.

Key words: Type 2 diabetes mellitus, PPARγ, Computer virtual screening, Molecular docking

刘治国, 崔文强, 魏叶, 朱心瑶, 杨雪. 基于计算机分子模拟技术从天然产物中筛选PPARγ激动剂[J]. 国际医药卫生导报, 2022, 28(9): 1204-1208.

Liu Zhiguo, Cui Wenqiang, Wei Ye, Zhu Xinyao, Yang Xue. Screening of PPARγ agonists from natural products based on computer molecular modeling[J]. International Medicine and Health Guidance News, 2022, 28(9): 1204-1208.

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基于计算机分子模拟技术从天然产物中筛选PPARγ激动剂

Screening of PPARγ agonists from natural products based on computer molecular modeling

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