Prediction of Signed Protein Kinase Regulatory Circuits
Prediction of Signed Protein Kinase Regulatory Circuits
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DOI：
10.1016/j.cels.2020.04.005
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发表时间：
2020-05-20
期刊：
影响因子：
9.3
通讯作者：
Beltrao, Pedro
中科院分区：
文献类型：
--
作者：
Invergo, Brandon M.;Petursson, Borgthor;Beltrao, Pedro
蛋白激酶之间复杂的调控关系网络是细胞内信号传导的主要组成部分。虽然许多激酶-激酶调节关系已被详细描述，但这些往往仅限于充分研究的激酶，而大多数可能的关系仍未探索。在这里，我们实现了一种数据驱动的监督机器学习方法来预测人类激酶-激酶调节关系以及它们是否具有激活或抑制作用。我们结合高通量数据，激酶特异性概况和结构信息来产生我们的预测。这些结果成功地概括了先前注释的调控关系，并可以从头开始重建已知的信号通路。整个预测网络相对稀疏，绝大多数关系的概率都很低。然而，尽管如此，它表明更密集的模式间激酶调节比通常认为在细胞内信号研究。本文的透明同行评审过程的记录包含在补充信息中。
Complex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these tend to be limited to well-studied kinases whereas the majority of possible relationships remains unexplored. Here, we implement a data-driven, supervised machine learning method to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research. A record of this paper's transparent peer review process is included in the Supplemental Information.