Protein Kinase Ontology (ProKinO) is a collaborative effort between the Evolutionary Systems Biology Group Lab of Dr. Natarajan Kannan at the Biochemistry and Molecular Biology Department and Dr. Krys J. Kochut's lab at the School of Computing, both at the University of Georgia, Athens, USA. Dr. Gurinder Gosal, formerly a student in Computer Science, UGA, created an initial version of the software system to automatically populate ProKinO from the selected data sources (see Data sources, below). The population system has been subsequently enhanced by our students Dr. Shima Dastgheib, formerly Computer Science, UGA, Dr. Daniel McSkimming, formerly IOB, UGA, and Dr. Abbas Keshavarzi, formerly Computer Science, UGA, and several other current and former lab members (see People, below).

The conceptual representation of such diverse information in one place, in the form of the ProKinO knowledge graph, enables not only rapid discovery of significant information related to a specific protein kinase, but also enables large scale integrative analysis of the protein kinase family. An outline of the data organization in ProKinO is represented using a UML class diagram (see Schema, below).

Publications:

Soleymani, S., Gravel, N., Huang, L. C., Yeung, W., Bozorgi, E., Bendzunas, N. G., Kochut, K.J., and Kannan, N. (2022). Dark kinase annotation, mining and visualization using the Protein Kinase Ontology. bioRxiv, 2022-02. doi: https://doi.org/10.1101/2022.02.25.482021

Salcedo, M., Gravel, N., Keshavarzi, A., Huang, L., Kochut, K., Kannan, N. (2023). Predicting Protein and Pathway Associations for Understudied Dark Kinases using Pattern-constrained Knowledge Graph Embedding. PeerJ, under review

Yeung, W., Zhou, Z., Li, S., & Kannan, N. (2023). Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. Briefings in Bioinformatics, 24(1), bbac599.

McSkimming, D.I., Dastgheib, S., Talevich, E., Narayanan, A., Katiyar, S., Taylor, S.S., Kochut, K.J., Kannan, N. (2015). "ProKinO: a unified resource for mining the cancer kinome." Human Mutation 36(2):175-86. doi:10.1002/humu.22726.

Gosal, G., Kochut, K.J., Kannan, N. (2011). "ProKinO: An Ontology for Integrative Analysis of Protein Kinases in Cancer." PLoS ONE 6(12):e28782. doi:10.1371/journal.pone.0028782

Gosal, G., Kannan, N., Kochut, K.J. (2011). "ProKinO: A Framework for Protein Kinase Ontology," in Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 550-555.

Disease Data COSMIC (Catalogue of Somatic Mutations in Cancer) database ` is used as the source of information regarding kinase cancer mutations. In addition to mutations, other information such as, the primary sites, histology, samples, description and other relevant features is also obtained from COSMIC.

The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mutations In Cancer web site, http://www.sanger.ac.uk/cosmic Bamford et al (2004) The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer, 91,355-358.

Reactome project. http://www.reactome.org/ (26th January 2011).

The UniProt Consortium, Ongoing and future developments at the Universal Protein Resource Nucleic Acids Res. 39: D214-D219 (2011).

The Protein Kinase Complement of the Human Genome, G Manning, DB Whyte, R Martinez, T Hunter, S Sudarsanam (2002). Science 298:1912-1934.

1. Neuwald AF, Liu JS, Lawrence CE. Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Sci. 1995 Aug;4(8):1618-32. PubMed PMID:8520488; PubMed Central PMCID: PMC2143180.
2. Kannan N, Neuwald AF. Did protein kinase regulatory mechanisms evolve through elaboration of a simple structural component? J Mol Biol. 2005 Sep 2;351(5):956-72. PubMed PMID: 16051269.
3. Hanks SK, Hunter T. Protein kinases 6. The eukaryotic protein kinase superfamily: kinase (catalytic) domain structure and classification. FASEB J. 1995 May;9(8):576-96. Review. PubMed PMID:7768349.

You can use this ontology browser to quickly locate protein kinase proteins and a lot of information related to the proteins, including the sequence, structure, function, mutation and pathway information on kinases.

You may initiate ProKinO browsing by selecting one of the following items in the Browse menu:

• Organisms, which provides the list of organisms; select an organism of interest and click on it to display the kinase proteins for the organism. Subsequently, you can select and click one of the proteins to explore.
• Proteins, which provides the list of kinase proteins for many organisms; select a protein of interest and click on it to display the protein data.
• Kinase Domains, which provides the hierarchy all kinase domains; you will be able to expand the groups into families and subfamilies, eventually finding the domains; again, select one of interest and click on it to display the kinase domain information.
• Diseases, which provides the list of all diseases (limited to cancers at this time) that are affected by kinase proteins; select one of interest and click on it to display the cancer data.
• Functional Domains, which provides the list of all functional domains; again, you may select one of interest and click on it to display the functional domain data.

It is also possible to initiate ProKinO browsing by searching for a specific kinase protein, disease, pathway, or for any object. The searching uses a set of terms, possibly separated by OR, AND and AND NOT boolean connectors. It is possible to use wildcards, for example, FGFR*. Phrases should be enclosed within double quotes.

The Query functionality for Proteins, Arbitrary SPARQL queries can be submitted (however, the size of the resulting set is limited to 1500, at this time).

The ProKinO ontology has not been released for downloading, yet.

After you initiate ProKinO browsing by selecting and displaying the data information page for one of the proteins, kinase domains, diseases, or functional domains, you will be able to explore the ontology by activating the hyper-links leading to a variety of related data.

Usually, a data information page includes the properties of the shown entity (for example, cellular location or tissue specificity of the proteins), and links to other related ontology entities (for example, functional domains, sequences, pathways of the proteins).

The protein data is subdivided into sub-areas, including the general information, functional features, mutations, pathways, and external references. These sub-areas of information are available by clicking on the provided tabs. The mutations are further subdivided into substitutions, insertions, deletions, complex mutations, and other.