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Article

GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains

Key Laboratory of Molecular Biophysics of Ministry of Education, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Center for Artificial Intelligence Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Author to whom correspondence should be addressed.
The first two authors equally contributed to this study.
Cells 2020, 9(5), 1266; https://doi.org/10.3390/cells9051266
Received: 22 April 2020 / Revised: 17 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
Protein phosphorylation is essential for regulating cellular activities by modifying substrates at specific residues, which frequently interact with proteins containing phosphoprotein-binding domains (PPBDs) to propagate the phosphorylation signaling into downstream pathways. Although massive phosphorylation sites (p-sites) have been reported, most of their interacting PPBDs are unknown. Here, we collected 4458 known PPBD-specific binding p-sites (PBSs), considerably improved our previously developed group-based prediction system (GPS) algorithm, and implemented a deep learning plus transfer learning strategy for model training. Then, we developed a new online service named GPS-PBS, which can hierarchically predict PBSs of 122 single PPBD clusters belonging to two groups and 16 families. By comparison, GPS-PBS achieved a highly competitive accuracy against other existing tools. Using GPS-PBS, we predicted 371,018 mammalian p-sites that potentially interact with at least one PPBD, and revealed that various PPBD-containing proteins (PPCPs) and protein kinases (PKs) can simultaneously regulate the same p-sites to orchestrate important pathways, such as the PI3K-Akt signaling pathway. Taken together, we anticipate GPS-PBS can be a great help for further dissecting phosphorylation signaling networks. View Full-Text
Keywords: protein phosphorylation; phosphoprotein-binding domain; phosphorylation site; PPBD-specific binding p-site; deep learning; protein kinase protein phosphorylation; phosphoprotein-binding domain; phosphorylation site; PPBD-specific binding p-site; deep learning; protein kinase
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MDPI and ACS Style

Guo, Y.; Ning, W.; Jiang, P.; Lin, S.; Wang, C.; Tan, X.; Yao, L.; Peng, D.; Xue, Y. GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains. Cells 2020, 9, 1266. https://doi.org/10.3390/cells9051266

AMA Style

Guo Y, Ning W, Jiang P, Lin S, Wang C, Tan X, Yao L, Peng D, Xue Y. GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains. Cells. 2020; 9(5):1266. https://doi.org/10.3390/cells9051266

Chicago/Turabian Style

Guo, Yaping; Ning, Wanshan; Jiang, Peiran; Lin, Shaofeng; Wang, Chenwei; Tan, Xiaodan; Yao, Lan; Peng, Di; Xue, Yu. 2020. "GPS-PBS: A Deep Learning Framework to Predict Phosphorylation Sites that Specifically Interact with Phosphoprotein-Binding Domains" Cells 9, no. 5: 1266. https://doi.org/10.3390/cells9051266

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