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Cited by 13Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksSemantic Web and Ontologies

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

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Soleymani, Saber, Gravel, Nathan, Huang, Liang-Chin, Yeung, Wayland, Bozorgi, Elika, Bendzunas, Nathaniel G., Kochut, Krzysztof J., Kannan, Natarajan·2023·PeerJ
DOI 10.7717/peerj.16087PMC PMC10704995

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Abstract

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO’s capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. 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 kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO’s potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables 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 https://prokino.uga.edu .

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Metadata

Authors
Soleymani, S., Gravel, N., Huang, L.-C., Yeung, W., Bozorgi, E., Bendzunas, N. G., Kochut, K. J., & Kannan, N.
Affiliations
University of Georgia (US)
Corresponding author
  • Kochut, Krzysztof J.
  • Kannan, Natarajan
ORCID
Venue
PeerJ
Publisher
PeerJ
Published
2023-12-05
Volume
11
Pages
e16087
ISSN
2167-8359 (ISSN-L)
DOI
10.7717/peerj.16087
PMC
PMC10704995
OpenAlex
W4389340627
Cited by
13
References
4
Citations by year
2026: 2 · 2025: 5 · 2024: 2 · 2023: 3
FWCI
1.07
Type
journal article
Language
en
Open access
Licence
Locations
  • PeerJ (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (U01CA239106 and R35 GM139656)
Topics
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
knowledge graphs, PAK5, oncogenic mutations, acute myeloid leukemia, ligand binding sites, kinase activation loop, AlphaFold models, cancer mutations
MeSH terms
Humans, Ligands, Neoplasms, Phosphorylation, Protein Kinases, Proteins

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