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利用计算机筛选和等温滴定量热法发现MDM2小分子抑制剂

Application of In Silico Filtering and Isothermal Titration Calorimetry for the Discovery of Small Molecule Inhibitors of MDM2.

作者信息

Alali Hen, Bloch Itai, Rapaport Irena, Rodrigues Luisa, Sher Inbal, Ansbacher Tamar, Gal Maayan

机构信息

Department of Oral Biology, Goldschleger School of Dental Medicine, Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.

Migal-Galilee Research Institute, Tel-Hai Academic College, Upper Galilee 12210, Israel.

出版信息

Pharmaceuticals (Basel). 2022 Jun 16;15(6):752. doi: 10.3390/ph15060752.

Abstract

The initial discovery phase of protein modulators, which consists of filtering molecular libraries and in vitro direct binding validation, is central in drug discovery. Thus, virtual screening of large molecular libraries, together with the evaluation of binding affinity by isothermal calorimetry, generates an efficient experimental setup. Herein, we applied virtual screening for discovering small molecule inhibitors of MDM2, a major negative regulator of the tumor suppressor p53, and thus a promising therapeutic target. A library of 20 million small molecules was screened against an averaged model derived from multiple structural conformations of MDM2 based on published structures. Selected molecules originating from the computational filtering were tested in vitro for their direct binding to MDM2 via isothermal titration calorimetry. Three new molecules, representing distinct chemical scaffolds, showed binding to MDM2. These were further evaluated by exploring structure-similar chemical analogues. Two scaffolds were further evaluated by de novo synthesis of molecules derived from the initial molecules that bound MDM2, one with a central oxoazetidine acetamide and one with benzene sulfonamide. Several molecules derived from these scaffolds increased wild-type p53 activity in MCF7 cancer cells. These set a basis for further chemical optimization and the development of new chemical entities as anticancer drugs.

摘要

蛋白质调节剂的初始发现阶段包括筛选分子文库和进行体外直接结合验证,这在药物发现中至关重要。因此,对大分子文库进行虚拟筛选,并通过等温滴定量热法评估结合亲和力,构成了一个高效的实验方案。在此,我们应用虚拟筛选来发现MDM2的小分子抑制剂,MDM2是肿瘤抑制因子p53的主要负调控因子,因此是一个有前景的治疗靶点。针对基于已发表结构的MDM2多种结构构象推导的平均模型,对一个包含2000万个小分子的文库进行了筛选。对计算筛选出的分子进行体外测试,通过等温滴定量热法检测它们与MDM2的直接结合情况。三个代表不同化学骨架的新分子显示出与MDM2结合。通过探索结构相似的化学类似物对它们进行了进一步评估。通过从头合成源自与MDM2结合的初始分子的分子,对其中两个骨架进行了进一步评估,一个含有中心氧杂氮杂环丁烷乙酰胺,另一个含有苯磺酰胺。从这些骨架衍生出的几个分子在MCF7癌细胞中提高了野生型p53的活性。这些为进一步的化学优化以及开发作为抗癌药物的新化学实体奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8307/9230431/302b486dce10/pharmaceuticals-15-00752-g001.jpg

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