Suppr超能文献

选择性A2A和A2B腺苷受体拮抗剂的3D药效团模型

3D-pharmacophore models for selective A2A and A2B adenosine receptor antagonists.

作者信息

Wei Jing, Wang Songqing, Gao Shaofen, Dai Xuedong, Gao Qingzhi

机构信息

School of Pharmaceutical Science and Technology, Tianjin University, 92 Weijin Road, Nankai District, Tianjin 300072, P.R. China.

出版信息

J Chem Inf Model. 2007 Mar-Apr;47(2):613-25. doi: 10.1021/ci600410m. Epub 2007 Mar 2.

Abstract

Three-dimensional pharmacophore models were generated for A2A and A2B adenosine receptors (ARs) based on highly selective A2A and A2B antagonists using the Catalyst program. The best pharmacophore model for selective A2A antagonists (Hypo-A2A) was obtained through a careful validation process. Four features contained in Hypo-A2A (one ring aromatic feature (R), one positively ionizable feature (P), one hydrogen bond acceptor lipid feature (L), and one hydrophobic feature (H)) seem to be essential for antagonists in terms of binding activity and A2A AR selectivity. The best pharmacophore model for selective A2B antagonists (Hypo-A2B) was elaborated by modifying the Catalyst common features (HipHop) hypotheses generated from the selective A2B antagonists training set. Hypo-A2B also consists of four features: one ring aromatic feature (R), one hydrophobic aliphatic feature (Z), and two hydrogen bond acceptor lipid features (L). All features play an important role in A2B AR binding affinity and are essential for A2B selectivity. Both A2A and A2B pharmacophore models have been validated toward a wide set of test molecules containing structurally diverse selective antagonists of all AR subtypes. They are capable of identifying correspondingly high potent antagonists and differentiating antagonists between subtypes. The results of our study will act as a valuable tool for retrieving structurally diverse compounds with desired biological activities and designing novel selective adenosine receptor ligands.

摘要

基于高选择性A2A和A2B拮抗剂,使用Catalyst程序生成了A2A和A2B腺苷受体(ARs)的三维药效团模型。通过仔细的验证过程获得了选择性A2A拮抗剂的最佳药效团模型(Hypo-A2A)。Hypo-A2A中包含的四个特征(一个环状芳香特征(R)、一个可正电离特征(P)、一个氢键受体脂质特征(L)和一个疏水特征(H))对于拮抗剂的结合活性和A2A AR选择性似乎至关重要。通过修改从选择性A2B拮抗剂训练集生成的Catalyst通用特征(HipHop)假设,精心构建了选择性A2B拮抗剂的最佳药效团模型(Hypo-A2B)。Hypo-A2B也由四个特征组成:一个环状芳香特征(R)、一个疏水脂肪族特征(Z)和两个氢键受体脂质特征(L)。所有这些特征在A2B AR结合亲和力中都起着重要作用,并且对于A2B选择性至关重要。A2A和A2B药效团模型均已针对包含所有AR亚型结构多样的选择性拮抗剂的大量测试分子进行了验证。它们能够识别相应的高效拮抗剂并区分不同亚型的拮抗剂。我们的研究结果将成为检索具有所需生物活性的结构多样化合物以及设计新型选择性腺苷受体配体的宝贵工具。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验