应用化学 ›› 2018, Vol. 35 ›› Issue (7): 788-794.DOI: 10.11944/j.issn.1000-0518.2018.07.170332

• 研究论文 • 上一篇    下一篇

基于配体与受体结构的酪氨酸酶抑制剂定量构效关系分析

汤海峰abc,崔凤超a*(),刘伦洋ac,李云琦a*()   

  1. a中国科学院长春应用化学研究所,合成橡胶重点实验室 长春 130022
    b吉林大学生命科学学院 长春 130012
    c中国科学院大学 北京 100049
  • 收稿日期:2017-09-12 接受日期:2018-01-10 出版日期:2018-07-06 发布日期:2018-07-06
  • 通讯作者: 崔凤超,李云琦
  • 基金资助:
    国家自然科学基金 (21374117,21504092)和国家博士后科学基金(2014M561310)资助

Insight into the Inhibitory Activities of Diverse Ligands for Tyrosinase Using Ligand- and Structure-based Approaches

Haifeng TANGabc,Fengchao CUIa*(),Lunyang LIUac,Yunqi LIa*()   

  1. aKey Laboratory of Synthetic Rubber,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences,Changchun 130022,China
    bSchool of Life Science,Jilin University,Changchun 130012,China
    cUniversity of Chinese Academy of Sciences,Beijing 100049,China.
  • Received:2017-09-12 Accepted:2018-01-10 Published:2018-07-06 Online:2018-07-06
  • Contact: Fengchao CUI,Yunqi LI
  • Supported by:
    Supported by the National Natural Science Foundation of China(No.21374117, No.21504092), and the China Postdoctoral Science Foundation(No.2014M561310)

摘要:

酪氨酸酶是细胞内催化合成黑素的关键酶。 理解酪氨酸酶抑制剂结构与活性之间的关系对于设计新药和化妆品具有重要意义。 然而,酪氨酸酶抑制剂的定量构效关系仍不清楚。 本文利用配体和结构描述符构建了隐式和显式模型,阐明了酪氨酸酶抑制剂定量构效关系。 隐式模型的相关系数R高达0.961,显式模型的相关系数为0.775。 两个模型很好地预测了3个茶多酚的酪氨酸酶抑制活性,表儿茶素没食子酸酯(ECG)>表没食子儿茶素没食子酸酯(EGCG)>没食子酸(G)。 相关性分析发现,抑制剂与酪氨酸酶结合引起的构象熵损失与抑制剂的活性密切相关。 具有较少构象熵损失的ECG在4种茶多酚中具有较高酪氨酸酶抑制活性。 结合自由能计算也证实ECG与酪氨酸酶的结合能力最强。 此外,通过分解结合自由能发现,酪氨酸酶活性中心的氨基酸残基(His57、His201、Asn202、His205、Glu192和Val215)与抑制剂形成了较强的范德华和静电相互作用,进而稳定了复合物结构。

关键词: 酪氨酸酶, 定量构效关系, 分子力学-Poisson Boltzmann表面积方法, 随机森林, 蒙特卡罗

Abstract:

The use of variant inhibitors to regulate the bioactivities of tyrosinase, which is the key enzyme in charge of the production of melanin and pigments, is a long-standing approach to design cosmetic and pharmaceutical products. The quantitative description of the structure-activity relationship of tyrosinase inhibitors is still unclear. In this study, we constructed descriptive models by integrating ligand- and structure-based approaches for such purpose. They provide correlation coefficients of 0.961 for implicit models and 0.775 for explicit model, respectively, to descript the activities of three tea polyphenols with the tyrosinase inhibitory activity order of (-)-Epicatechin gallate(ECG)>(-)-Epigallocatechin gallate(EGCG)>Gallic acid(G). As revealing from the descriptive models, entropy loss is more important than other features for determining inhibitory activity and thus the tyrosinase-ECG complex with the fewer conformational entropy loss has the strongest inhibitory activity in vitro among the four tea polyphenols. Moreover, residues including His57, His201, Asn202, His205 Glu192 and Val215 are the core of active sites in tyrosinase, and stabilize the tyrosinase-inhibitor complex by van der Waals and hydrogen bonding interactions.

Key words: tyrosinase, quantitative structure-activity relationship, molecular mechanics/Poisson Boltzmann surface area, Random Forest algorithm, Monte Carlo algorithm