Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Guo Y

PROVIDER: S-EPMC7290655 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

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

Guo Yaping Y Ning Wanshan W Jiang Peiran P Lin Shaofeng S Wang Chenwei C Tan Xiaodan X Yao Lan L Peng Di D Xue Yu Y

Cells 20200520 5

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 deve ...[more]

PMID: 32443803

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Project description:[Structure: see text]. FHA domains are protein modules that switch signals in diverse biological pathways by monitoring the phosphorylation of threonine residues of target proteins. As part of the effort to gain insight into cellular avoidance of cancer, FHA domains involved in the cellular response to DNA damage have been especially well-characterized. The complete protein where the FHA domain resides and the interaction partners determine the nature of the signaling. Thus, a key biochemical question is how do FHA domains pick out their partners from among thousands of alternatives in the cell? This Account discusses the structure, affinity, and specificity of FHA domains and the formation of their functional structure. Although FHA domains share sequence identity at only five loop residues, they all fold into a beta-sandwich of two beta-sheets. The conserved arginine and serine of the recognition loops recognize the phosphorylation of the threonine targeted. Side chains emanating from loops that join beta-strand 4 with 5, 6 with 7, or 10 with 11 make specific contacts with amino acids of the ligand that tailor sequence preferences. Many FHA domains choose a partner in extended conformation, somewhat according to the residue three after the phosphothreonine in sequence (pT + 3 position). One group of FHA domains chooses a short carboxylate-containing side chain at pT + 3. Another group chooses a long, branched aliphatic side chain. A third group prefers other hydrophobic or uncharged polar side chains at pT + 3. However, another FHA domain instead chooses on the basis of pT - 2, pT - 3, and pT + 1 positions. An FHA domain from a marker of human cancer instead chooses a much longer protein fragment that adds a beta-strand to its beta-sheet and that presents hydrophobic residues from a novel helix to the usual recognition surface. This novel recognition site and more remote sites for the binding of other types of protein partners were predicted for the entire family of FHA domains by a bioinformatics approach. The phosphopeptide-dependent dynamics of an FHA domain, SH2 domain, and PTB domain suggest a common theme: rigid, preformed binding surfaces support van der Waals contacts that provide favorable binding enthalpy. Despite the lack of pronounced conformational changes in FHA domains linked to binding events, more subtle adjustments may be possible. In the one FHA domain tested, phosphothreonine peptide binding is accompanied by increased flexibility just outside the binding site and increased rigidity across the beta-sandwich. The folding of the same FHA domain progresses through near-native intermediates that stabilize the recognition loops in the center of the phosphoprotein-binding surface; this may promote rigidity in the interface and affinity for targets phosphorylated on threonine.

Dataset Information

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

Publications

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

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