Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

中文翻译：

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大规模化学蛋白质组学加速配体发现并预测细胞中的配体行为

蛋白质的化学调节可以实现对生物学的机械理解，并且代表了大多数疗法的基础。然而，尽管经过数十年的研究，80% 的人类蛋白质组仍缺乏功能性配体。化学蛋白质组学为细胞系统提供了先进的基于片段的配体发现，但通量限制阻碍了片段-蛋白质相互作用的可扩展鉴定。我们报告了 407 个结构不同的小分子片段的蛋白质结合倾向的蛋白质组图谱。我们验证了已识别的相互作用可以进一步发展为 E3 泛素连接酶、转运蛋白和激酶的活性化学探针。集成机器学习二元分类器进一步实现了细胞中片段行为的可解释预测。由此产生的片段-蛋白质相互作用和预测模型的资源将有助于阐明分子识别的原理，并加快迄今为止未药物化的蛋白质的配体发现工作。