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Selective Degradation of GSPT1 by Cereblon Modulators Identified via a Focused Combinatorial Library.
ACS Chemical Biology ( IF 3.5 ) Pub Date : 2020-08-31 , DOI: 10.1021/acschembio.0c00520
Chelsea E Powell 1, 2 , Guangyan Du 1, 2 , Jianwei Che 1, 2 , Zhixiang He 1, 2 , Katherine A Donovan 1, 2 , Hong Yue 1, 2 , Eric S Wang 1, 2 , Radosław P Nowak 1, 2 , Tinghu Zhang 1, 2 , Eric S Fischer 1, 2 , Nathanael S Gray 1, 2
Affiliation  

Cereblon (CRBN) is an E3 ligase adapter protein that can be reprogrammed by imide-class compounds such as thalidomide, lenalidomide, and pomalidomide to induce the degradation of neo-substrate proteins. In order to identify additional small molecule CRBN modulators, we implemented a focused combinatorial library approach where we fused an imide-based CRBN-binding pharmacophore to a heterocyclic scaffold, which could be further elaborated. We screened the library for CRBN-dependent antiproliferative activity in the multiple myeloma cell line MM1.S and identified five hit compounds. Quantitative chemical proteomics of hit compounds revealed that they induced selective degradation of GSPT1, a translation termination factor that is currently being explored as a therapeutic target for the treatment of acute myeloid leukemia. Molecular docking studies with CRBN and GSPT1 followed by analogue synthesis identified a possible hydrogen bond interaction with the central pyrimidine ring as a molecular determinant of hit compounds’ selectivity. This study demonstrates that a focused combinatorial library design, phenotypic screening, and chemical proteomics can provide a suitable workflow to efficiently identify novel CRBN modulators.

中文翻译:

通过聚焦组合库识别的 Cereblon 调制器选择性降解 GSPT1。

Cereblon (CRBN) 是一种 E3 连接酶衔接蛋白,可被沙利度胺、来那度胺和泊马度胺等酰亚胺类化合物重新编程,以诱导新底物蛋白的降解。为了鉴定额外的小分子 CRBN 调节剂,我们实施了一种集中组合库方法,我们将基于酰亚胺的 CRBN 结合药效团融合到杂环支架上,可以进一步阐述。我们在多发性骨髓瘤细胞系 MM1.S 中筛选了库中的 CRBN 依赖性抗增殖活性,并确定了五种命中化合物。命中化合物的定量化学蛋白质组学显示,它们诱导了 GSPT1 的选择性降解,GSPT1 是一种翻译终止因子,目前正在探索作为治疗急性髓性白血病的治疗靶点。用 CRBN 和 GSPT1 进行分子对接研究,然后进行类似物合成,确定了可能与中心嘧啶环的氢键相互作用是命中化合物选择性的分子决定因素。这项研究表明,集中的组合文库设计、表型筛选和化学蛋白质组学可以提供合适的工作流程来有效识别新型 CRBN 调节剂。
更新日期:2020-10-17
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