Suppr超能文献

利用分子建模方法鉴定腺苷受体 2A 的新型化学实体。

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches.

机构信息

Laboratory of Modeling and Computational Chemistry, Department of Biological and Health Sciences, Federal University of Amapá, 68902-280 Macapá-AP, Brazil.

Nucleus of Studies and Selection of Bioactive Molecules, Institute of Health Sciences, Federal University of Pará, 66075-110 Belém-PA, Brazil.

出版信息

Molecules. 2020 Mar 10;25(5):1245. doi: 10.3390/molecules25051245.

Abstract

Adenosine Receptor Type 2A (AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-AAR. For the search for AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC = 7.54407; ZINC04257548 pEC = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to AAR.

摘要

腺苷受体 2A(AAR)在许多重要的过程中发挥作用,如抗炎过程。因此,本研究旨在通过基于药效团的虚拟筛选来寻找化合物。化合物的药代动力学/毒理学特征,以及稳健的定量构效关系,通过分子对接预测了结合模式。最后,我们使用分子动力学研究了配体-AAR 相互作用的稳定性。为了寻找 AAR 激动剂,研究了 UK-432097 和 BindingDB 数据库中的一组 20 种化合物。这些化合物用于生成药效团模型。通过多元线性回归构建定量构效关系模型,预测生物活性,利用分子性质。最佳药效团模型用于在数据库中搜索商业化合物,并将基于药效团的虚拟筛选得到的化合物应用于定量构效关系。由于具有令人满意的药代动力学/毒理学特征和定量构效关系预测(Diverset 10002403 pEC = 7.54407;ZINC04257548 pEC = 7.38310),两种化合物显示出有希望的活性。此外,与阳性对照雷加德松(Lexiscan)相比,它们的对接和分子动力学结果也令人满意。这些化合物可以用于生物测定(体外和体内),以确认对 AAR 的潜在激动剂活性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b4f9/7179438/9ef978219533/molecules-25-01245-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验