通过泛素蛋白酶体系统进行的蛋白质降解在空间和时间上的调节对于许多细胞过程至关重要。 E3 连接酶和降解信号（降解决定子）是它们在靶蛋白中识别的序列，是泛素介导的蛋白水解的关键部分，它们的相互作用决定了降解特异性并维持细胞稳态。迄今为止，仅确定了有限数量的目标降解决定子实例，并且其特性尚未完全表征。为了应对这一挑战，我们在这里开发了一种新颖的深度学习框架，即 MetaDegron，用于通过集成蛋白质语言模型和全面的特征化策略来预测 E3 连接酶靶向降解子。通过使用基准数据集的广泛评估以及与 Degpred 等现有方法的比较，我们证明了 MetaDegron 的卓越性能。在功能特征中，MetaDegron 允许批量预测 21 种 E3 连接酶的目标降解决定子，并提供多个与降解决定子相关的结构和理化特征的功能注释和可视化。 MetaDegron 可在 http://modinfor.com/MetaDegron/ 上免费获取。我们预计 MetaDegron 将成为临床和转化界阐明蛋白质稳态调节机制、癌症研究和药物开发的有用工具。© 作者 2024。由牛津大学出版社出版。

Protein degradation through the ubiquitin proteasome system at the spatial and temporal regulation is essential for many cellular processes. E3 ligases and degradation signals (degrons), the sequences they recognize in the target proteins, are key parts of the ubiquitin-mediated proteolysis, and their interactions determine the degradation specificity and maintain cellular homeostasis. To date, only a limited number of targeted degron instances have been identified, and their properties are not yet fully characterized. To tackle on this challenge, here we develop a novel deep-learning framework, namely MetaDegron, for predicting E3 ligase targeted degron by integrating the protein language model and comprehensive featurization strategies. Through extensive evaluations using benchmark datasets and comparison with existing method, such as Degpred, we demonstrate the superior performance of MetaDegron. Among functional features, MetaDegron allows batch prediction of targeted degrons of 21 E3 ligases, and provides functional annotations and visualization of multiple degron-related structural and physicochemical features. MetaDegron is freely available at http://modinfor.com/MetaDegron/. We anticipate that MetaDegron will serve as a useful tool for the clinical and translational community to elucidate the mechanisms of regulation of protein homeostasis, cancer research, and drug development.© The Author(s) 2024. Published by Oxford University Press.