Suppr超能文献

针对靶向 DNA 修复-复制界面和 SARS-CoV-2 的核酸酶的结构为基础的抑制剂的高效化学筛选方法。

An efficient chemical screening method for structure-based inhibitors to nucleic acid enzymes targeting the DNA repair-replication interface and SARS CoV-2.

机构信息

Department of Cancer Biology, University of Texas MD Anderson Cancer Center, Houston, TX, United States; Department of Molecular & Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States.

出版信息

Methods Enzymol. 2021;661:407-431. doi: 10.1016/bs.mie.2021.09.003. Epub 2021 Sep 27.

Abstract

We present a Chemistry and Structure Screen Integrated Efficiently (CASSIE) approach (named for Greek prophet Cassandra) to design inhibitors for cancer biology and pathogenesis. CASSIE provides an effective path to target master keys to control the repair-replication interface for cancer cells and SARS CoV-2 pathogenesis as exemplified here by specific targeting of Poly(ADP-ribose) glycohydrolase (PARG) and ADP-ribose glycohydrolase ARH3 macrodomains plus SARS CoV-2 nonstructural protein 3 (Nsp3) Macrodomain 1 (Mac1) and Nsp15 nuclease. As opposed to the classical massive effort employing libraries with large numbers of compounds against single proteins, we make inhibitor design for multiple targets efficient. Our compact, chemically diverse, 5000 compound Goldilocks (GL) library has an intermediate number of compounds sized between fragments and drugs with predicted favorable ADME (absorption, distribution, metabolism, and excretion) and toxicological profiles. Amalgamating our core GL library with an approved drug (AD) library, we employ a combined GLAD library virtual screen, enabling an effective and efficient design cycle of ranked computer docking, top hit biophysical and cell validations, and defined bound structures using human proteins or their avatars. As new drug design is increasingly pathway directed as well as molecular and mechanism based, our CASSIE approach facilitates testing multiple related targets by efficiently turning a set of interacting drug discovery problems into a tractable medicinal chemistry engineering problem of optimizing affinity and ADME properties based upon early co-crystal structures. Optimization efforts are made efficient by a computationally-focused iterative chemistry and structure screen. Thus, we herein describe and apply CASSIE to define prototypic, specific inhibitors for PARG vs distinct inhibitors for the related macrodomains of ARH3 and SARS CoV-2 Nsp3 plus the SARS CoV-2 Nsp15 RNA nuclease.

摘要

我们提出了一种化学和结构综合高效筛选(CASSIE)方法(以希腊先知卡珊德拉命名),用于设计癌症生物学和发病机制的抑制剂。CASSIE 为靶向癌症细胞修复-复制接口的关键钥匙以及 SARS-CoV-2 发病机制提供了一条有效途径,这里通过对聚(ADP-核糖)糖水解酶(PARG)和 ADP-核糖糖水解酶 ARH3 宏结构域以及 SARS-CoV-2 非结构蛋白 3(Nsp3)宏结构域 1(Mac1)和 Nsp15 核酸酶的特异性靶向为例进行了说明。与传统的大量使用含有大量化合物的文库针对单个蛋白质的方法不同,我们使针对多个目标的抑制剂设计更加高效。我们的紧凑、化学多样性的 5000 化合物 Goldilocks(GL)文库具有介于片段和药物之间的化合物数量,具有预测有利的 ADME(吸收、分布、代谢和排泄)和毒理学特征。将我们的核心 GL 文库与已批准的药物(AD)文库合并,我们采用了组合的 GLAD 文库虚拟筛选,从而能够有效地设计排名靠前的计算机对接、顶级生物物理和细胞验证以及使用人蛋白或其替身定义结合结构的循环,并对排名靠前的候选物进行深入的结构优化。由于新药设计越来越具有途径导向性以及分子和机制基础,因此我们的 CASSIE 方法通过将一组相互作用的药物发现问题有效地转化为基于亲和力和 ADME 特性的可管理的药物化学工程问题,从而促进了对多个相关靶点的测试。优化工作通过以计算为重点的迭代化学和结构筛选来实现高效。因此,我们在此描述并应用 CASSIE 来定义 PARG 的原型、特异性抑制剂,以及与 ARH3 和 SARS-CoV-2 Nsp3 的相关宏结构域以及 SARS-CoV-2 Nsp15 RNA 核酸酶的独特抑制剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/782a/8474023/ae0cbd8b0b75/f18-01-9780323907330_lrg.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