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

被引:5
作者
Shu, Yixuan [1 ]
Hai, Yanru [1 ]
Cao, Lihua [1 ]
Wu, Jianmin [1 ,2 ]
机构
[1] Peking Univ, Canc Hosp & Inst, Ctr Canc Bioinformat, Key Lab Carcinogenesis & Translat Res,Minist Educ, Beijing 100142, Peoples R China
[2] Peking Univ, Int Canc Inst, Beijing 100191, Peoples R China
基金
中国国家自然科学基金;
关键词
Ubiquitination; Deep learning; Ubiquitin proteasome system; E3-substrate interactions; DUB-substrate interactions; Pan-cancer analysis; COMPREHENSIVE RESOURCE; DRUG DISCOVERY;
D O I
10.1016/j.csbj.2023.01.021
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:1014 / 1021
页数:8
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