We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A protein sequence-based deep transfer learning framework for identifying human proteome-wide deubiquitinase-substrate interactions.
- Authors
Liu, Yuan; Li, Dianke; Zhang, Xin; Xia, Simin; Qu, Yingjie; Ling, Xinping; Li, Yang; Kong, Xiangren; Zhang, Lingqiang; Cui, Chun-Ping; Li, Dong
- 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. The specificity of protein deubiquitination relies on deubiquitinase-substrate interactions (DSIs). Here, authors leverage evolutionary information from the proteome to predict DSIs, even with an inadequate training dataset.
- Subjects
DEEP learning; PROTEIN-protein interactions; PROTEINS; LEARNING strategies; DEUBIQUITINATING enzymes; DRUG target
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-48446-3