Pharmacophore development, drug-likeness analysis, molecular docking, and molecular dynamics simulations for identification of new CK2 inhibitors.

Protein kinase 2 (CK2), an essential serine/threonine casein kinase, is considered an interesting target for cancer treatments. Different molecular modeling approaches such as pharmacophore modeling, molecular docking, and molecular dynamics simulations have been used to develop new CK2 inhibitors. This study presents a pharmacophore model that was generated by combining and merging the structure-based and ligand-based pharmacophore features and validated using receiver operating characteristic (ROC). Based on validation results revealing good predictive ability, this pharmacophore model was used as a three-dimensional query in a virtual screening simulation. Several compounds with different chemical scaffolds were retrieved as hits, which were further analyzed and refined using several molecular property filters. The obtained compounds were then filtered and compared to the crystallographic ligand on the basis of their predicted docking energies, binding mode, and interactions with CK2 active site residues. This step resulted in a compound with a high pharmacophore fit value and better docking energy. Molecular dynamics simulation indicated stable binding of the predicted compound to CK2 protein, characterized by root mean square deviation (RMSD) and root mean square fluctuation (RMSF) and hydrogen bond. Graphical abstract.