Accurate prediction of kinase-substrate networks using knowledge graphs

NOVÁČEK, Vít, Gavin MCGAURAN, David MATALLANAS, Adrián Vallejo BLANCO, Piero CONCA, Emir MUÑOZ, Luca COSTABELLO, Kamalesh KANAKARAJ, Zeeshan NAWAZ, Brian WALSH, Sameh K MOHAMED, Pierre-Yves VANDENBUSSCHE, Colm J RYAN, Walter KOLCH and Dirk FEY. Accurate prediction of kinase-substrate networks using knowledge graphs. PLoS Computational Biology. Cambridge: PLoS, 2020, vol. 16, No 12, p. 1-30. ISSN 1553-734X. Available from: https://dx.doi.org/10.1371/journal.pcbi.1007578.

Basic information

• Original name: Accurate prediction of kinase-substrate networks using knowledge graphs
• Authors: NOVÁČEK, Vít (203 Czech Republic, guarantor, belonging to the institution), Gavin MCGAURAN, David MATALLANAS, Adrián Vallejo BLANCO, Piero CONCA, Emir MUÑOZ, Luca COSTABELLO, Kamalesh KANAKARAJ, Zeeshan NAWAZ, Brian WALSH, Sameh K MOHAMED, Pierre-Yves VANDENBUSSCHE, Colm J RYAN, Walter KOLCH and Dirk FEY.
• Edition: PLoS Computational Biology, Cambridge, PLoS, 2020, 1553-734X.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL URL
• Impact factor: Impact factor: 4.475
• RIV identification code: RIV/00216224:14330/20:00118443
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1371/journal.pcbi.1007578
• UT WoS: 000597151200003
• Keywords in English: knowledge graphs; kinase-substrate networks; phosphorylation prediction; relational machine learning
• Tags: kinase-substrate networks, knowledge graphs, phosphorylation prediction, relational machine learning
• Tags: International impact, Reviewed

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).