| Autor: |
Zhongjie Liang, Tonghai Liu, Qi Li, Guangyu Zhang, Bei Zhang, Xikun Du, Jingqiu Liu, Zhifeng Chen, Hong Ding, Guang Hu, Hao Lin, Fei Zhu, Cheng Luo |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Cell Reports, Vol 42, Iss 9, Pp 113048- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2211-1247 |
| DOI: |
10.1016/j.celrep.2023.113048 |
| Popis: |
Summary: Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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