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基于定量磷酸化蛋白质组学的分子网络描述用于高分辨率激酶-底物相互作用组分析。

Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis.

机构信息

Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan.

Medical Proteomics Laboratory, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan.

出版信息

Bioinformatics. 2016 Jul 15;32(14):2083-8. doi: 10.1093/bioinformatics/btw164. Epub 2016 Mar 24.

Abstract

MOTIVATION

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 we develop and evaluate a platform to provide a high-resolution molecular network description for kinase-substrate interactome analysis.

RESULTS

In this study, we 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.

AVAILABILITY AND IMPLEMENTATION

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.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

磷酸化依赖的细胞信号转导在调节多种细胞过程(如增殖、分化和凋亡)中起着多样化的作用,这是已知的。基于质谱的磷酸化蛋白质组学的最新技术进步使我们能够以全面和定量的方式测量全网络信号动力学。由于基于传统蛋白质-蛋白质相互作用(PPI)信息的网络分析不足以系统地分析磷酸化位点依赖性的复杂相互作用动力学,因此我们开发并评估了一个平台,为激酶-底物相互作用组分析提供高分辨率的分子网络描述。

结果

在这项研究中,我们开发了一个基于 Cytoscape 的生物信息学平台,名为“翻译后修饰映射器(PTMapper)”,该平台将 PPI 数据与可公开获得的磷酸化氨基酸残基分辨率的激酶-底物关系整合在一起。以前关于 EGF 诱导的神经胶质瘤干细胞中细胞信号的磷酸蛋白质组数据被应用于评估我们的平台,从而发现了 p70S6K1 相关途径中磷酸化依赖性关键信号调节。我们的研究表明,磷酸化位点依赖性激酶-底物信号调节的高分辨率细胞网络描述应该加速基于磷酸蛋白质组学的在每个疾病相关信号背景下对新型药物靶点的探索。

可用性和实施

PTMapper 和用于构建磷酸化位点导向网络的示例数据可在 https://github.com/y-narushima/PTMapper 上获得。

联系人

moyama@ims.u-tokyo.ac.jp

补充信息

补充数据可在生物信息学在线获得。

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