A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions

Yuan Liu, Dianke Li, Xin Zhang, Simin Xia, Yingjie Qu, Xinping Ling, Yang Li, Xiangren Kong, Lingqiang Zhang, Chun-Ping Cui () and Dong Li ()
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Yuan Liu: Beijing Institute of Lifeomics
Dianke Li: Beijing Institute of Lifeomics
Xin Zhang: Beijing Institute of Lifeomics
Simin Xia: Beijing Institute of Lifeomics
Yingjie Qu: Beijing Institute of Lifeomics
Xinping Ling: Beijing Institute of Lifeomics
Yang Li: Beijing Institute of Lifeomics
Xiangren Kong: Beijing Institute of Lifeomics
Lingqiang Zhang: Beijing Institute of Lifeomics
Chun-Ping Cui: Beijing Institute of Lifeomics
Dong Li: Beijing Institute of Lifeomics

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Protein ubiquitination regulates a wide range of cellular processes. The degree of protein ubiquitination is determined by the delicate balance between ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase (DUB)-mediated deubiquitination. In comparison to the E3-substrate interactions, the DUB-substrate interactions (DSIs) remain insufficiently investigated. To address this challenge, we introduce a protein sequence-based ab initio method, TransDSI, which transfers proteome-scale evolutionary information to predict unknown DSIs despite inadequate training datasets. An explainable module is integrated to suggest the critical protein regions for DSIs while predicting DSIs. TransDSI outperforms multiple machine learning strategies against both cross-validation and independent test. Two predicted DUBs (USP11 and USP20) for FOXP3 are validated by “wet lab” experiments, along with two predicted substrates (AR and p53) for USP22. TransDSI provides new functional perspective on proteins by identifying regulatory DSIs, and offers clues for potential tumor drug target discovery and precision drug application.

Date: 2024
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DOI: 10.1038/s41467-024-48446-3

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