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.

蛋白激酶之间调节关系的复杂网络包括细胞内信号传导的主要组成部分。尽管已经详细描述了许多激酶-激酶调节关系，但是它们倾向于仅限于经过充分研究的激酶，而大多数可能的关系尚待探索。在这里，我们实现了一种数据驱动的有监督的机器学习方法，以预测人类激酶-激酶调节关系以及它们是否具有激活或抑制作用。我们结合了高通量数据，激酶特异性概况和结构信息来产生我们的预测。结果成功地概括了先前注释的调节关系，并且可以从头开始重建已知的信号传导途径。完整的预测网络相对稀疏，绝大多数关系都分配了低概率。然而，尽管如此，它暗示了比细胞内信号研究中通常所考虑的更紧密的激酶间调节模式。补充信息中包含了本文透明的同行评审过程的记录。