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Updated: Dec 23, 2025
Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach
Published on: February 21, 2019
Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors
Lindsey Burggraaff1, Eelke B Lenselink1, Willem Jespers1,2
1Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
Abstract:
Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of <60% (at a concentration of 10 μM) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC50 value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.
Insights
This study introduces a novel workflow for kinase inhibitor design, successfully identifying new RET kinase inhibitors. This approach aids in discovering novel chemical matter for multitarget drug design and polypharmacology modeling.
Area of Science:
- Medicinal Chemistry
- Computational Drug Discovery
- Pharmacology
Background:
- Kinases play crucial roles in cellular processes and are key targets for anticancer drug development.
- Multitarget drug design aims to inhibit multiple kinases simultaneously for enhanced therapeutic effects.
- Developing selective kinase inhibitors requires sophisticated modeling approaches.
Purpose of the Study:
- To present and validate a computational workflow for modeling kinase bioactivity spectra.
- To identify novel kinase inhibitors with specific selectivity profiles.
- To explore the application of this workflow in multitarget drug design and polypharmacology.
Main Methods:
- Development and benchmarking of statistical and structure-based models for kinase inhibition.
- Virtual screening using the developed workflow to identify inhibitors for RET kinase.
- Experimental validation of identified inhibitors for RET kinase activity and potency.
Main Results:
- The workflow successfully identified 5 novel and chemically diverse RET kinase inhibitors.
- The most potent inhibitor exhibited modest activity with a pIC50 value of 5.1.
- Inhibitors showed low similarity to known RET inhibitors, indicating novel chemical matter.
Conclusions:
- The presented multitarget workflow effectively detects novel kinase inhibitors.
- This approach is applicable to polypharmacology modeling and identifying new chemical matter for existing targets.
- The workflow can be readily adapted for other kinase targets in drug discovery.
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