PO.CH03.01 · 化学

使用LiP-MS对分子胶进行全蛋白质组范围的靶点结合和三元复合物图谱分析

Proteome-wide target engagement and ternary complex mapping of molecular glues using LiP-MS

海报缩略图:使用LiP-MS对分子胶进行全蛋白质组范围的靶点结合和三元复合物图谱分析
编号 2417 展板 6 时间 4/20 09:00–12:00 区域 Section 39 主讲 Yuehan Feng, PhD
分会场 Structural and Chemical Biology
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作者与单位 Authors & Affiliations

Martin Soste1, Polina Shichkova1, Matevz Stefancic1, Daniel Redfern1, Francesca Cavallo2, Lorna Charge2, Ka Ying Lee2, Ricardo Canavate del Pino2, Denise Swift2, Roland Hjerpe2, Stuart Thomson2, Allan Jordan2, Yuehan Feng1

1Biognosys AG, Schlieren, Switzerland,2Sygnature Discovery, Nottingham, United Kingdom

摘要 Abstract

中文摘要
在天然细胞环境中破译小分子的分子靶点和相互作用网络,对于基于靶点和基于表型的药物发现均至关重要。这对于分子胶(MG)尤为关键,分子胶通过诱导的蛋白-蛋白相互作用促进选择性蛋白降解。定义MG作用的两条机制轴:(1)化合物直接结合E3连接酶,后者随后招募新底物;(2)化合物结合一个主要蛋白靶点,从而促进E3连接酶的招募和三元复合物的形成。 有限蛋白水解偶联质谱(LiP-MS)已成为一种强大的、无标记的方法,可在复杂蛋白质组中阐明小分子靶点结合并绘制结合位点图谱,而无需化学标记或遗传操作。在此,我们将LiP-MS的应用扩展到两种互补的实验形式,以探究分子胶活性的不同阶段。 在第一种情形中,实施了一种裂解液内LiP-MS工作流程,以识别整个蛋白质组范围内的主要药物-蛋白相互作用。使用定量数据非依赖性采集质谱(DIA-MS)和七点浓度系列,我们监测了来自超过8,000个蛋白的超过250,000条肽段的构象和可及性变化。基于机器学习的LiP评分实现了结合位点的肽段级分辨率和靶点结合的定量排序。 在第二种情形中,开发了一种活细胞LiP-MS分析,以在生理条件下捕获化合物诱导的蛋白-蛋白相互作用变化。这种活细胞形式能够检测继发性的、化合物依赖性的蛋白招募事件,包括与E3连接酶及其他相关蛋白形成三元复合物。 为评估LiP-MS在表征分子胶机制方面的性能,我们使用两种代表性化合物进行了全局靶点鉴定实验:SR-4835,一种结合CDK12并招募DDB1的cyclin K降解剂;以及MRT-2359,一种结合CRBN的GSPT1降解剂。活细胞LiP-MS实验在两个时间点(1小时和6小时)进行,以监测与主要靶点结合及潜在三元复合物形成相关的化合物依赖性构象变化和蛋白招募动力学。 总之,LiP-MS提供了一个全面、高分辨率的平台,可直接在细胞环境中绘制小分子靶点结合图谱,并表征与分子胶活性相关的动态蛋白招募事件。
查看英文原文 English abstract
Deciphering molecular targets and interaction networks of small molecules within native cellular contexts remains essential for both target-based and phenotypic drug discovery. This is particularly critical for molecular glues (MGs), which promote selective protein degradation through induced protein-protein interactions. Two mechanistic axes define MG action: (1) direct binding of the compound to an E3 ligase that subsequently recruits a neosubstrate, and (2) compound engagement with a primary protein target that promotes E3 ligase recruitment and ternary complex formation. Limited proteolysis coupled with mass spectrometry (LiP-MS) has emerged as a powerful, label-free approach for elucidating small-molecule target engagement and mapping binding sites in complex proteomes without chemical tagging or genetic manipulation. Here, we expand the application of LiP-MS to two complementary experimental formats that interrogate distinct stages of molecular glue activity. In the first scenario, an in-lysate LiP-MS workflow was implemented to identify primary drug-protein interactions across the proteome. Using quantitative data-independent acquisition mass spectrometry (DIA-MS) and a seven-point concentration series, we monitored conformational and accessibility changes across >250,000 peptides from >8,000 proteins. Machine learning-based LiP scoring enabled peptide-level resolution of binding sites and quantitative ranking of target engagement. In the second scenario, a live-cell LiP-MS assay was developed to capture compound-induced protein-protein interaction changes under physiological conditions. This live-cell format enables detection of secondary, compound-dependent protein recruitment events, including ternary complex formation with E3 ligases and other associated proteins. To evaluate LiP-MS performance in characterizing molecular glue mechanisms, we conducted global target identification experiments using two representative compounds: SR-4835, a cyclin K degrader that binds CDK12 and recruits DDB1, and MRT-2359, a GSPT1 degrader that engages CRBN. Live-cell LiP-MS experiments were performed at two time points (1 hour and 6 hours) to monitor compound-dependent conformational changes and protein recruitment dynamics associated with primary target engagement and potential ternary complex formation. Together, LiP-MS provides a comprehensive, high-resolution platform for mapping small-molecule target engagement and for characterizing dynamic protein recruitment events associated with molecular glue activity directly in the cellular environment.
利益披露 Disclosure
M. Soste, None.. P. Shichkova, None.. M. Stefancic, None.. D. Redfern, None.. F. Cavallo, None.. L. Charge, None.. K. Lee, None.. R. Canavate del Pino, None.. D. Swift, None.. R. Hjerpe, None.. S. Thomson, None.. A. Jordan, None.. Y. Feng, None.

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