Suppr超能文献

设计、3D-QSAR 建模及 TGF-β 型 I 抑制剂与靶点(癌症)对接。

Design, 3D QSAR modeling and docking of TGF-β type I inhibitors to target cancer.

机构信息

Immunology Laboratory, Department of Pharmacy, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India.

Drug Discovery laboratory, Department of Chemistry, Annamalai University, Annamalai Nagar 608002, Tamil Nadu, India.

出版信息

Comput Biol Chem. 2018 Oct;76:232-244. doi: 10.1016/j.compbiolchem.2018.07.011. Epub 2018 Jul 24.

Abstract

Transforming growth factor-β (TGF-β) family members plays a vital role in regulating hormonal function, bone formation, tissue remodeling, and erythropoiesis, cell growth and apoptosis. TGF-β super-family members mediate signal transduction via serine/threonine kinase receptors located on the cell membrane. Variation in expression of the TGF-β type I and II receptors in the cancer cells compromise its tumor suppressor activities which direct to oncogenic functions. The present study was aimed to screen the potent TGF-β type I inhibitors through atom based 3D-QSAR and pharmacophore modelling. For this purpose, we have chosen known TGF-β type I inhibitors from the binding database. The PHASE module of Schrodinger identified the best Pharmacophore model which includes three hydrogen bond acceptors (A), one hydrophobic region (H), and one ring (R) as the structural features. The top pharmacophore model AAAHR.27 was chosen with the R value of 0.94 and validated externally using molecules of the test set. Moreover the AAAHR.27 model underwent virtual screening using the molecules from the NCI, ZINC and Maybridge database. The screened molecules were further filtered using molecular docking and ADME properties prediction. Additionally, the 7 lead molecules were compared with a newly identified compound "SB431542" (well known TGF-β type I receptor inhibitor) and Galunisertib, a small molecule inhibitor of TGF-β type I, under clinical development (phase II trials) using the docking score and other binding properties. Also a top scored screened molecule from our study has been compared and confirmed using molecular dynamic simulation studies. By this way, we have obtained 7 distinct drug-like TGF-β type I inhibitors which can be beneficial in suppressing cancers reported with up-regulation of TGF-β type I. This result highlights the guidelines for designing molecules with TGF-β Type I inhibitory properties.

摘要

转化生长因子-β(TGF-β)家族成员在调节激素功能、骨形成、组织重塑和红细胞生成、细胞生长和凋亡方面发挥着重要作用。TGF-β超家族成员通过位于细胞膜上的丝氨酸/苏氨酸激酶受体介导信号转导。癌细胞中 TGF-β 型 I 和 II 受体表达的变化使其肿瘤抑制活性丧失,从而导致致癌功能。本研究旨在通过基于原子的 3D-QSAR 和药效团建模筛选有效的 TGF-β 型 I 抑制剂。为此,我们从结合数据库中选择了已知的 TGF-β 型 I 抑制剂。Schrodinger 的 PHASE 模块确定了最佳药效团模型,该模型包括三个氢键受体(A)、一个疏水区(H)和一个环(R)作为结构特征。选择了具有 R 值为 0.94 的最佳药效团模型 AAAHR.27,并使用测试集的分子进行外部验证。此外,使用 NCI、ZINC 和 Maybridge 数据库中的分子对 AAAHR.27 模型进行了虚拟筛选。筛选出的分子进一步使用分子对接和 ADME 性质预测进行过滤。此外,将这 7 个先导分子与新鉴定的化合物“SB431542”(一种众所周知的 TGF-β 型 I 受体抑制剂)和 Galunisertib(一种处于临床开发阶段(II 期试验)的 TGF-β 型 I 小分子抑制剂)进行比较,使用对接评分和其他结合性质。我们的研究中还比较和确认了一个得分最高的筛选分子使用分子动力学模拟研究。通过这种方式,我们获得了 7 种不同的具有药物样特性的 TGF-β 型 I 抑制剂,这些抑制剂可能有助于抑制 TGF-β 型 I 上调的癌症。这一结果突出了设计具有 TGF-β 型 I 抑制特性的分子的指导原则。

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