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 a Dirk FEY. Accurate prediction of kinase-substrate networks using knowledge graphs. PLoS Computational Biology. Cambridge: PLoS, 2020, roč. 16, č. 12, s. 1-30. ISSN 1553-734X. Dostupné z: https://dx.doi.org/10.1371/journal.pcbi.1007578.

Základní údaje

• Originální název: Accurate prediction of kinase-substrate networks using knowledge graphs
• Autoři: NOVÁČEK, Vít (203 Česká republika, garant, domácí), 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 a Dirk FEY.
• Vydání: PLoS Computational Biology, Cambridge, PLoS, 2020, 1553-734X.

Další údaje

• Originální jazyk: angličtina
• Typ výsledku: Článek v odborném periodiku
• Obor: 10201 Computer sciences, information science, bioinformatics
• Stát vydavatele: Velká Británie a Severní Irsko
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL URL
• Impakt faktor: Impact factor: 4.475
• Kód RIV: RIV/00216224:14330/20:00118443
• Organizační jednotka: Fakulta informatiky
• Doi: http://dx.doi.org/10.1371/journal.pcbi.1007578
• UT WoS: 000597151200003
• Klíčová slova anglicky: knowledge graphs; kinase-substrate networks; phosphorylation prediction; relational machine learning
• Štítky: kinase-substrate networks, knowledge graphs, phosphorylation prediction, relational machine learning
• Příznaky: Mezinárodní význam, Recenzováno

Anotace

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).