Accurate prediction of kinase-substrate networks using knowledge graphs.

Vít Nováček,Sameh K Mohamed,David Matallanas,Adrián Vallejo Blanco,Piero Conca,Colm J Ryan,Kamalesh Kanakaraj,Zeeshan Nawaz,Dirk Fey,Emir Muñoz,Walter Kolch,Luca Costabello,Pierre-Yves Vandenbussche,Brian Walsh,Gavin Mcgauran,Anand R Asthagiri

doi:10.1371/journal.pcbi.1007578

Abstract

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).

Highlights

Most aspects of cell behaviour are controlled by phosphorylation events and intricate networks of kinases-substrate relationships mediating these phosphorylations [1]
LinkPhinder is a new approach to prediction of protein signalling networks based on kinase-substrate relationships that outperforms existing approaches
We show that our model has superior predictive power based on a comparative validation trial following standard machine learning evaluatuion protocols

Summary

Introduction

Most aspects of cell behaviour are controlled by phosphorylation events and intricate networks of kinases-substrate relationships mediating these phosphorylations [1]. Depending on the phosphorylation site, the attachment of a phosphate group can alter the activity of a substrate, its interaction with other proteins or its subcellular localization. This diversity of phosphorylation mediated processes control important cellular functions such as signal transduction, differentiation, migration, cell division and apoptosis. Dysregulation of these kinasesubstrate relationships can have devastating consequences and are regularly observed in prevalent diseases, such as cancers or immune diseases. High-throughput experiments are not informative in this case, because they cannot establish these detailed functional relationships, and addressing this issue in a one-by-one fashion is prohibitively expensive and time-consuming due to the large number of candidate interactions to be tested [6]

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Conclusion