# A data analytics framework for mining the dark kinome

> **NIH NIH U01** · UNIVERSITY OF GEORGIA · 2020 · $438,510

## Abstract

Project Summary
The overall goal of this proposal is to generate and experimentally test models of understudied “dark” kinase
evolution and function, and to develop a data-analytics framework for hypothesis generation and testing on the
dark kinome. Our working hypothesis is that integrative mining of available sequence, structure, and functional
data on the entire kinome from diverse organisms (both well-studied and dark kinases) will provide important
context for defining sequence and structural features associated with dark kinase functions. As a preliminary
test of our hypothesis, we have integrated and conceptualized diverse forms of data related to protein kinase
structure, function, and evolution in the form of the Protein Kinase Ontology (ProKinO), and successfully
demonstrated the application of an ontological framework in identifying key knowledge gaps in the human
kinome and in discovering key residues/motifs associated with protein kinase regulation. We propose to build
on these successful studies to accomplish the following two aims. Aim1 will develop a novel comparative
kinomics framework in which natural and disease variants in dark kinases will be correlated and visualized in
the context of PTMs and protein-protein interactions to investigate the relationships connecting sequence,
structure, function and regulation. Models of functional specialization will be experimentally tested in selected
dark and pseudokinases using biochemical and cell-based assays and made publically available in human and
machine-readable format, adhering to Findable, Accessible, Interoperable and Reusable (FAIR) data rules.
Aim2 will build a unique framework for complex aggregate queries on semantically linked protein kinase data
from disparate sources and formats using graphical, easy to use interfaces. Researchers will interact with the
framework and formulate queries based on a familiar and intuitive view of the data. A knowledge map-based
interactive query interface will be developed using which researchers can interact with ProKinO using
semantics they use and understand. ProKinO will be formally linked with Pharos, the Drug Target Ontology
(DTO) and the Protein Ontology (PRO) to expand community outreach and user base.
The proposed studies are expected to provide a unified data analytics framework for knowledge discovery and
hypothesis generation on the dark kinome and enhance the ability of the Illuminating the Druggable Genome
(IDG) consortium to make accurate predictions about the physiological roles of dark kinases. The proposed
integration of ProKinO with DTO and PRO will enhance the application of these ontologies in drug discovery
and provide open source software for building data instantiated ontologies for other IDG targets such as ion-
channels and GPCRs. These outcomes, in turn, are expected to accelerate the functional characterization of
the druggable “dark” proteome and address the IDG initiative of translating genomic data into knowle...

## Key facts

- **NIH application ID:** 9915864
- **Project number:** 5U01CA239106-02
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** Natarajan Kannan
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $438,510
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9915864

## Citation

> US National Institutes of Health, RePORTER application 9915864, A data analytics framework for mining the dark kinome (5U01CA239106-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9915864. Licensed CC0.

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