Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

IF 6.9 2区 化学 Q1 SPECTROSCOPY Mass Spectrometry Reviews Pub Date : 2022-09-26 DOI:10.1002/mas.21808
Sander R. Piersma, Andrea Valles-Marti, Frank Rolfs, Thang V. Pham, Alex A. Henneman, Connie R. Jiménez
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Abstract

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

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从磷酸蛋白组数据推断激酶活性:工具比较与最新应用
细胞信号通路失常是癌症和其他疾病的标志。最重要的信号传导机制之一涉及蛋白质磷酸化/去磷酸化。蛋白磷酸化由蛋白激酶催化,人类基因组中已发现 530 多种蛋白激酶。激酶活性异常是肿瘤发生和癌症进展的驱动因素之一,会导致下游底物的磷酸化丰度发生变化。上游激酶的活性可以从磷酸化底物的全球集合中推断出来。如今,基于质谱的磷酸化蛋白质组实验可对每个生物样本中超过 10k 个磷酸化位点进行常规鉴定和定量。利用激酶底物富集分析(KSEA)、翻译后修饰底物富集分析(PTM-SEA)和整合推断激酶活性分析(INKA)等工具,可以利用这些底物磷酸化足迹推断上游激酶的活性。由于激酶活性推断这一主题非常活跃,过去三年中报道了许多新方法,因此我们想对这一领域做一个概述。在这篇综述中,我们介绍了激酶活性推断工具的清单、基本算法、统计框架、激酶底物数据库以及用户友好性。对使用最广泛的工具进行了深入比较。随后,重点介绍了这些工具在临床组织和血液样本中的最新应用。激酶活性推断工具有两个主要应用领域。(1) 通过使用多种互补工具(如 PTM-SEA 和 KSEA 或 INKA)进行分组比较,可以从大型数据集中获得最大的生物学洞察力。 (2) 在肿瘤学领域,个性化治疗需要对单个样本进行分析,例如,INKA 已成为一种工具,可以优先选择可操作的激酶进行靶向抑制。
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来源期刊
Mass Spectrometry Reviews
Mass Spectrometry Reviews 物理-光谱学
CiteScore
16.30
自引率
3.00%
发文量
56
期刊介绍: The aim of the journal Mass Spectrometry Reviews is to publish well-written reviews in selected topics in the various sub-fields of mass spectrometry as a means to summarize the research that has been performed in that area, to focus attention of other researchers, to critically review the published material, and to stimulate further research in that area. The scope of the published reviews include, but are not limited to topics, such as theoretical treatments, instrumental design, ionization methods, analyzers, detectors, application to the qualitative and quantitative analysis of various compounds or elements, basic ion chemistry and structure studies, ion energetic studies, and studies on biomolecules, polymers, etc.
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