Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract Phosphorylation-dependent cellular signaling is known to play a diverse role in regulating multiple cellular processes such as proliferation, differentiation and apoptosis. Recent technological advances in mass spectrometry-based phosphoproteomics have enabled us to measure network-wide signaling dynamics in a comprehensive and quantitative manner. As conventional protein-protein interaction (PPI) information-based network analysis is insufficient to systematically analyze phosphorylation site-dependent complex interaction dynamics, here  develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis. In this study,  developed a Cytoscape-based bioinformatical platform named 'Post Translational Modification mapper (PTMapper)' to integrate PPI data with publicly available kinase-substrate relations at the resolution of phosphorylated amino acid residues. The previous phosphoproteome data on EGF-induced cellular signaling in glioblastoma stem cells was applied to evaluate our platform, leading to discovery of phosphorylation-dependent crucial signaling modulation in the p70S6K1-related pathway. Our study revealed that high-resolution cellular network description of phosphorylation-site dependent kinase-substrate signaling regulation should accelerate phosphoproteomics-based exploration of novel drug targets in the context of each disease-related signaling. PTMapper and the example data for construction of phosphorylation site-oriented networks are available at https://github.com/y-narushima/PTMapper CONTACT: moyama@ims.u-tokyo.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.
['Yuta Narushima', 'Hiroko Kozuka-Hata', 'Kouhei Tsumoto', 'Jun-ichiro Inoue', 'Masaaki Oyama']
I am writing a research paper on a idea Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis
[{"from": "human", "value": "I am writing a research paper on a idea Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract Phosphorylation-dependent cellular signaling is known to play a diverse role in regulating multiple cellular processes such as proliferation, differentiation and apoptosis. Recent technological advances in mass spectrometry-based phosphoproteomics have enabled us to measure network-wide signaling dynamics in a comprehensive and quantitative manner. As conventional protein-protein interaction (PPI) information-based network analysis is insufficient to systematically analyze phosphorylation site-dependent complex interaction dynamics, here  develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis. In this study,  developed a Cytoscape-based bioinformatical platform named 'Post Translational Modification mapper (PTMapper)' to integrate PPI data with publicly available kinase-substrate relations at the resolution of phosphorylated amino acid residues. The previous phosphoproteome data on EGF-induced cellular signaling in glioblastoma stem cells was applied to evaluate our platform, leading to discovery of phosphorylation-dependent crucial signaling modulation in the p70S6K1-related pathway. Our study revealed that high-resolution cellular network description of phosphorylation-site dependent kinase-substrate signaling regulation should accelerate phosphoproteomics-based exploration of novel drug targets in the context of each disease-related signaling. PTMapper and the example data for construction of phosphorylation site-oriented networks are available at https://github.com/y-narushima/PTMapper CONTACT: moyama@ims.u-tokyo.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online."}]