Accurate Prediction of Kinase-Substrate Networks Using Knowledge Graphs

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).

蛋白激酶对特定底物的磷酸化作用，是调控关键细胞命运决定及其他细胞生命过程的核心控制机制。然而，发掘特定激酶-底物（kinase-substrate）相互作用关系不仅耗时耗力，且往往带有较强的偶然性。计算预测可缓解此类难题，但现有方法存在诸多局限，如激酶组（kinome）覆盖范围有限、预测精度不足，且通常仅利用局部特征，未能体现更广泛的相互作用背景。为解决上述局限，本研究开发了一款新型预测模型：该模型将磷酸化网络视为知识图谱（knowledge graph），并在此基础上采用统计关系学习方法，是一种简洁且鲁棒的网络化知识表示模型。与选取的六款现有主流系统相比，本模型拥有最广的激酶组覆盖范围，且可生成其他工具无法实现的、具有生物学有效性的高可信度预测结果。具体而言，本研究通过实验验证了人类细胞中LATS1、AKT1、PKA及MST2激酶介导的若干此前未被发现的磷酸化反应预测结果。因此，本工具可用于指导磷酸化蛋白质组（phosphoproteomic）实验的设计与开展，助力新型磷酸化反应的发现。本模型可通过一款操作简便的网页界面（LinkPhinder）公开访问。