Suppr超能文献

配体结合模式预测通过对接:mdm2/mdmx 抑制剂作为案例研究。

Ligand binding mode prediction by docking: mdm2/mdmx inhibitors as a case study.

机构信息

Department of Structural Biology and ‡Department of Chemical Biology and Therapeutics, Saint Jude Children's Research Hospital , 262 Danny Thomas Place, Memphis, Tennessee 38105, United States.

出版信息

J Chem Inf Model. 2014 Feb 24;54(2):648-59. doi: 10.1021/ci4004656. Epub 2014 Jan 21.

Abstract

The p53-binding domains of Mdm2 and Mdmx, two negative regulators of the tumor suppressor p53, are validated targets for cancer therapeutics, but correct binding poses of some proven inhibitors, particularly the nutlins, have been difficult to obtain with standard docking procedures. Virtual screening pipelines typically draw from a database of compounds represented with 1D or 2D structural information from which one or more 3D conformations must be generated. These conformations are then passed to a docking algorithm that searches for optimal binding poses on the target protein. This work tests alternative pipelines using several commonly used conformation generation programs (LigPrep, ConfGen, MacroModel, and Corina/Rotate) and docking programs (GOLD, Glide, MOE-dock, and AutoDock Vina) for their ability to reproduce known poses for a series of Mdmx and/or Mdm2 inhibitors, including several nutlins. Most combinations of these programs using default settings fail to find correct poses for the nutlins but succeed for all other compounds. Docking success for the nutlin class requires either computationally intensive conformational exploration or an "anchoring" procedure that incorporates knowledge of the orientation of the central imidazoline ring.

摘要

Mdm2 和 Mdmx 的 p53 结合域是肿瘤抑制因子 p53 的两个负调节剂,是癌症治疗的有效靶点,但一些已证明的抑制剂,特别是 nutlins 的正确结合构象,用标准对接程序很难获得。虚拟筛选管道通常从化合物数据库中提取,该数据库使用 1D 或 2D 结构信息表示,其中必须生成一个或多个 3D 构象。然后,这些构象被传递给对接算法,该算法在目标蛋白上搜索最佳结合构象。这项工作测试了使用几种常用构象生成程序(LigPrep、ConfGen、MacroModel 和 Corina/Rotate)和对接程序(GOLD、Glide、MOE-dock 和 AutoDock Vina)的替代管道,以测试它们在重现一系列 Mdmx 和/或 Mdm2 抑制剂(包括几种 nutlins)的已知构象方面的能力。这些程序的大多数组合使用默认设置都无法为 nutlins 找到正确的构象,但对所有其他化合物都成功。nutlin 类的对接成功需要计算密集型构象探索,或采用“锚定”程序,该程序结合了对中央咪唑啉环方向的了解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f366/4753531/8cce6a3a85ae/nihms642812f1.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