Dark kinase annotation, mining and visualization using the Protein Kinase Ontology

Abstract

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships connecting protein kinase sequence, structure, function, and disease in a human and machine-readable format. Here we extend the scope of ProKinO as a discovery tool by including new classes and relationships capturing information on kinase ligand binding sites, expression patterns, and functional features, and demonstrate its application in uncovering new knowledge regarding understudied members of the protein kinase family. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinase in human cancers with abnormal expression in multiple cancers, including an unappreciated role in acute myeloid leukemia. We identify recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation and identify common ligand/drug binding residues in PAK family kinases, highlighting the potential application of ProKinO in drug discovery. The updated ontology browser and a web component, ProtVista, which allows interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at http://prokino.uga.edu/browser/.

Evolutionary and cellular analysis of the ‘dark’ pseudokinase PSKH2

“…However, PSKH2 has been suggested to exhibit low-level synthetic lethality with the RAS oncogene [ 19 ], consistent with a role in proliferative signalling. PSKH2 is also a frequently mutated ‘dark’ pseudokinase in human cancers, with the majority of mutations mapping to specific regions in the N-terminal domain and the pseudokinase domain [ 20 ].…”

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

“…Despite these efforts, nearly 164 out of the 534 known kinases remain relatively understudied and are referred to as “dark” kinases by the NIH Illuminating the Druggable Genome consortium (IDG) ( Berginski et al, 2021 ; Gillespie et al, 2022 ; Kelleher et al, 2023 ). Characterizing the functions of these dark kinases is crucial because they work in conjunction with other well-studied kinases in signaling pathways and are also frequently mutated, or abnormally expressed, in human diseases such as cancers ( Berginski et al, 2021 ; Brognard & Hunter, 2011 ; Collins et al, 2018 ; Ravanmehr et al, 2021 ; Soleymani et al, 2022 ). While considerable progress has been made in illuminating the functions of several dark kinases, placing these kinases in a pathway or a cell signaling network context remains a major bioinformatics challenge.…”

“…The compendium of genomic, proteomic and interactome data now available through the efforts of the IDG consortium and numerous investigator-initiated efforts allows for the possibility of inferring the functions and pathways for dark kinases through integrative mining of the known patterns and relationships in existing data. In particular, the development of network-based approaches, such as knowledge graphs (KGs), that relate and link diverse types of protein kinase data in the forms of networks (composed of nodes and edges) enables the prediction of dark kinase functions or pathways through network context ( Soleymani et al, 2022 ). Indeed, KG mining approaches and machine learning (ML) methods applied to KGs have been successfully employed in the identification of kinase substrates ( Gavali et al, 2022 ; Nováček et al, 2020 ), prioritization of understudied kinases ( Huang et al, 2018 ), and identification of disease associations ( Bachman, Gyori & Sorger, 2022 ; Gyori et al, 2017 ; Moret et al, 2021 ; Soleymani et al, 2022 ).…”