A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

Abstract

Fragment-Based Drug Discovery (FBDD) has become, in recent years, a consolidated approach in the drug discovery process, leading to several drug candidates under investigation in clinical trials and some approved drugs. Among these successful applications of the FBDD approach, kinases represent a class of targets where this strategy has demonstrated its real potential with the approved kinase inhibitor Vemurafenib. In the Kinase family, protein kinase CK1 isoform δ (CK1δ) has become a promising target in the treatment of different neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. In the present work, we set up and applied a computational workflow for the identification of putative fragment binders in large virtual databases. To validate the method, the selected compounds were tested in vitro to assess the CK1δ inhibition.

Keywords: fragment-based drug discovery; molecular docking; molecular dynamics; protein kinase CK1δ; supervised molecular dynamics.

Figure 1

Schematic representation of the workflow adopted in the present work. First the fragments are retrieved from several vendors libraries. After proper preparation, the database is docked using three different docking protocols. the resulting poses have been filtered using a pharmacophore model and only the molecule that fit the model for each protocol have been retained. The poses of these molecules were further refined using MD to assess the stability of the binding mode. the molecules that appear to be stable were finally selected trough visual inspection.

Figure 2

Representation of the pharmacophore model used in the present work. Some representative crystallographic ligands are displayed (not all for clarity). The Pharmacophore model is formed by an aromatic ring (the three orange spheres define the position and its orientation) and two hydrogen bonds with the backbone of Leu85 (an acceptor and one donor).

Figure 3

CK1δ residual activity at a concentration of 100 μM of the ligand under examination. The molecules marked with a star (*) has been tested at 50 μM due to solubility issues.

Figure 4

CK1δ Residual activity at a concentration of 40 μM of the ligands that showed a residual activity of less than 40% at 100 μM.

Figure 5

SuMD simulation of compound 28. In panel (A) the interaction energy landscape is reported for the recognition trajectory displaying the ligand–protein interaction energy plotted against the distances between the protein–ligand center of mass. In panel (B), the superposition of the VS-pose (cyan) for compound 28 against the lowestenergy frame from the SuMD trajectory (orange).

Figure 6

The structure and binding mode for the seven compounds for which the IC50 value is reported. The value of IC50 is based on the average of three independent measurements.