从组学数据集中提取人类激酶-底物关系的数据驱动。

IF 6.1 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Benjamin Dominik Maier, Borgthor Petursson, Alessandro Lussana, Evangelia Petsalaki
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引用次数: 0

摘要

磷酸化是细胞用于决策和调控细胞分裂和分化等过程的信号系统的重要组成部分。在人类中,b> 90%已鉴定的磷酸位点没有有关上游激酶的注释。同时,大约30%的激酶(Uniprot注释)没有已知的靶标。这种知识差距强调了进行大规模、数据驱动的计算预测的必要性。在这项研究中,我们创建了一个基于机器学习的模型,从组学数据集中导出概率激酶-底物网络。与其他最先进的激酶-底物预测方法相比,我们的方法显示出更高的性能,并提供了更多激酶的预测。重要的是,它更好地捕获了新的实验鉴定的激酶-底物关系。因此,它可以改善激酶-底物对的优先级,以照亮黑暗的人类细胞信号传导空间。我们的模型集成到web服务器SELPHI2.0中,允许对磷蛋白质组学数据进行无偏分析,促进下游实验的设计,以揭示不同条件和细胞背景下的信号转导机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-driven extraction of human kinase-substrate relationships from omics datasets.

Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as cell division and differentiation. In human, >90% of identified phosphosites don't have annotations regarding the relevant upstream kinase. At the same time around 30% of kinases (as annotated in Uniprot) have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this study, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. Our methodology displays improved performance compared to other state-of-the-art kinase-substrate prediction methods and provides predictions for more kinases. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space. Our model is integrated into a web server, SELPHI2.0, to allow unbiased analysis of phosphoproteomics data, facilitating the design of downstream experiments to uncover mechanisms of signal transduction across conditions and cellular contexts.

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来源期刊
Molecular & Cellular Proteomics
Molecular & Cellular Proteomics 生物-生化研究方法
CiteScore
11.50
自引率
4.30%
发文量
131
审稿时长
84 days
期刊介绍: The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes
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