CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria.
Ersilia Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom.
J Am Chem Soc. 2023 Feb 8;145(5):2711-2732. doi: 10.1021/jacs.2c11098. Epub 2023 Jan 27.
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
只有大约 20%的人类蛋白质组被认为可以用小分子拮抗剂进行药物开发。这使得一些最引人注目的治疗靶点无法通过配体发现来实现。靶向蛋白降解(TPD)的概念有望克服这些限制。简而言之,TPD 依赖于诱导目标蛋白(POI)与 E3 泛素连接酶之间接近的小分子,导致 POI 的泛素化和降解。在这篇观点文章中,我们希望反思该领域当前的挑战,并讨论多组学分析、人工智能和机器学习(AI/ML)的进展将如何在克服这些挑战方面发挥至关重要的作用。所提出的路线图是在小分子降解剂的背景下讨论的,但同样适用于其他新兴的诱导接近模式。