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精细化的药效特征用于人血栓素 A2 受体拮抗剂的虚拟筛选。

Refined pharmacophore features for virtual screening of human thromboxane A2 receptor antagonists.

机构信息

Key Laboratory of Structure-Based Drug Design &Discovery of Ministry of Education, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China; School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, People's Republic of China.

School of Pharmacy, Shenyang Pharmaceutical University,Shenyang 110016, People's Republic of China.

出版信息

Comput Biol Chem. 2020 Jun;86:107249. doi: 10.1016/j.compbiolchem.2020.107249. Epub 2020 Mar 13.

DOI:10.1016/j.compbiolchem.2020.107249
PMID:32199335
Abstract

For a long time, the structural basis of TXA2 receptor is limited due to the lack of crystal structure information, till the release of the crystal structure of TXA2 receptor, which deepens our understanding about ligand recognition and selectivity mechanisms of this physiologically important receptor. In this research, we report the successful implementation in the discovery of an optimal pharmacophore model of human TXA2 receptor antagonists through virtual screening. Structure-based pharmacophore models were generated based on two crystal structures of human TXA2 receptor (PDB entry 6IIU and 6IIV). Docking simulation revealed interaction modes of the virtual screening hits against TXA2 receptor, which was validated through molecular dynamics simulation and binding free energy calculation. ADMET properties were also analyzed to evaluate the toxicity and physio-chemical characteristics of the hits. The research would provide valuable insight into the binding mechanisms of TXA2 receptor antagonists and thus be helpful for designing novel antagonists.

摘要

长期以来,由于缺乏晶体结构信息,TXA2 受体的结构基础一直受到限制,直到 TXA2 受体的晶体结构被公布,这加深了我们对这种重要生理受体的配体识别和选择性机制的理解。在这项研究中,我们通过虚拟筛选成功地实现了人 TXA2 受体拮抗剂的最佳药效团模型的发现。基于两个晶体结构的人 TXA2 受体(PDB 条目 6IIU 和 6IIV)生成了基于结构的药效团模型。对接模拟揭示了虚拟筛选命中物与 TXA2 受体的相互作用模式,通过分子动力学模拟和结合自由能计算进行了验证。还分析了 ADMET 性质,以评估命中物的毒性和物理化学特性。该研究将为 TXA2 受体拮抗剂的结合机制提供有价值的见解,从而有助于设计新型拮抗剂。

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