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使用磷酸化蛋白质组学数据的激酶活性排名(KARP)可量化蛋白激酶对细胞活力调节的贡献。

Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability.

作者信息

Wilkes Edmund H, Casado Pedro, Rajeeve Vinothini, Cutillas Pedro R

机构信息

From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ.

From the ‡Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ

出版信息

Mol Cell Proteomics. 2017 Sep;16(9):1694-1704. doi: 10.1074/mcp.O116.064360. Epub 2017 Jul 3.

Abstract

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.

摘要

细胞存活受由蛋白激酶活性驱动的信号网络调控;然而,确定该网络中每个激酶对这种调控的贡献仍然具有挑战性。在此,我们报告一种计算方法,该方法使用基于质谱的磷酸化蛋白质组学数据,根据激酶对细胞调控的贡献对蛋白激酶进行排名。我们发现,该算法返回的分数,我们将其称为利用磷酸化蛋白质组学数据的激酶活性排名(KARP),是单个激酶对信号输出贡献的定量度量。将KARP应用于八种血液学细胞系的分析表明,细胞周期蛋白依赖性激酶(CDK)1/2、酪蛋白激酶(CK)2、细胞外信号调节激酶(ERK)和p21激活激酶(PAK)是这些细胞模型中最常排名靠前的激酶。激酶激活模式具有细胞系特异性,但与作为激酶抑制剂处理函数的细胞活力显示出显著关联。因此,我们的研究例证了KARP作为一种无靶向方法,可凭经验和系统地识别信号网络中的调控激酶。

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本文引用的文献

1
An atlas of human kinase regulation.人类激酶调控图谱。
Mol Syst Biol. 2016 Dec 1;12(12):888. doi: 10.15252/msb.20167295.
4
Unmasking determinants of specificity in the human kinome.揭示人类激酶组中特异性的决定因素。
Cell. 2015 Sep 24;163(1):187-201. doi: 10.1016/j.cell.2015.08.057. Epub 2015 Sep 17.
7
Empirical inference of circuitry and plasticity in a kinase signaling network.激酶信号网络中电路和可塑性的经验性推断
Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):7719-24. doi: 10.1073/pnas.1423344112. Epub 2015 Jun 9.
8
PhosphoSitePlus, 2014: mutations, PTMs and recalibrations.磷酸化位点Plus,2014:突变、翻译后修饰与重新校准。
Nucleic Acids Res. 2015 Jan;43(Database issue):D512-20. doi: 10.1093/nar/gku1267. Epub 2014 Dec 16.
9
10
A draft map of the human proteome.人类蛋白质组草图。
Nature. 2014 May 29;509(7502):575-81. doi: 10.1038/nature13302.

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