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基于深度学习的方法来识别人类E3泛素连接酶和去泛素化酶的底物。

Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases.

作者信息

Shu Yixuan, Hai Yanru, Cao Lihua, Wu Jianmin

机构信息

Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Center for Cancer Bioinformatics, Peking University Cancer Hospital & Institute, Beijing 100142, China.

Peking University International Cancer Institute, Peking University, Beijing 100191, China.

出版信息

Comput Struct Biotechnol J. 2023 Jan 18;21:1014-1021. doi: 10.1016/j.csbj.2023.01.021. eCollection 2023.

Abstract

E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs) play key roles in protein degradation. However, a large number of E3 substrate interactions (ESIs) and DUB substrate interactions (DSIs) remain elusive. Here, we present DeepUSI, a deep learning-based framework to identify ESIs and DSIs using the rich information present in protein sequences. Utilizing the collected golden standard dataset, key hyperparameters in the process of model training, including the ones relevant to data sampling and number of epochs, have been systematically assessed. The performance of DeepUSI was thoroughly evaluated by multiple metrics, based on internal and external validation. Application of DeepUSI to cancer-associated E3 and DUB genes identified a list of druggable substrates with functional implications, warranting further investigation. Together, DeepUSI presents a new framework for predicting substrates of E3 ubiquitin ligases and deubiquitinates.

摘要

E3泛素连接酶(E3s)和去泛素化酶(DUBs)在蛋白质降解中起关键作用。然而,大量的E3底物相互作用(ESIs)和DUB底物相互作用(DSIs)仍然不清楚。在此,我们提出了DeepUSI,这是一个基于深度学习的框架,用于利用蛋白质序列中存在的丰富信息来识别ESIs和DSIs。利用收集到的金标准数据集,系统地评估了模型训练过程中的关键超参数,包括与数据采样和轮次数量相关的参数。基于内部和外部验证,通过多个指标对DeepUSI的性能进行了全面评估。将DeepUSI应用于癌症相关的E3和DUB基因,确定了一系列具有功能意义的可成药底物,值得进一步研究。总之,DeepUSI为预测E3泛素连接酶和去泛素化酶的底物提供了一个新框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f942/9883182/4c6fd531c0a6/ga1.jpg

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