研究人员报告了407种结构不同的小分子片段的全蛋白质组蛋白质结合倾向图谱。研究人员验证了已识别的相互作用可以推进到E3泛素连接酶、转运体和激酶的活性化学探针。通过整合机器学习二元分类器，研究人员进一步对片段在细胞中的行为做出了可解释的预测。由此产生的片段-蛋白质相互作用资源和预测模型，将有助于阐明分子识别的原理，并加快迄今为止尚未药物化的蛋白质的配体发现工作。
据了解，通过对蛋白质进行化学调节，可以从机理上了解生物学，这也是大多数疗法的基础。然而，尽管进行了数十年的研究，人类蛋白质组中仍有80%缺乏功能配体。化学蛋白质组学推动了细胞系统中基于片段的配体发现，但通量的限制阻碍了片段与蛋白质相互作用的规模化鉴定。
附：英文原文
Title: Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells
Author: Fabian Offensperger, Gary Tin, Miquel Duran-Frigola, Elisa Hahn, Sarah Dobner, Christopher W. am Ende, Joseph W. Strohbach, Andrea Rukavina, Vincenth Brennsteiner, Kevin Ogilvie, Nara Marella, Katharina Kladnik, Rodolfo Ciuffa, Jaimeen D. Majmudar, S. Denise Field, Ariel Bensimon, Luca Ferrari, Evandro Ferrada, Amanda Ng, Zhechun Zhang, Gianluca Degliesposti, Andras Boeszoermenyi, Sascha Martens, Robert Stanton, André C. Müller, J. Thomas Hannich, David Hepworth, Giulio Superti-Furga, Stefan Kubicek, Monica Schenone, Georg E. Winter
Issue&Volume: 2024-04-26
Abstract: 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.
DOI: adk5864
Source: https://www.science.org/doi/10.1126/science.adk5864