Over expression of Protein kinase (CK2) suppresses apoptosis induced by a variety of agents, whereas down-regulation of CK2 sensitizes cells to induction of apoptosis. In this study, we have built quantitative structure activity relationship (QSAR) models, which were trained and tested on experimentally verified 38 enzyme׳s inhibitors having inhibitory value IC50 in µM. These inhibitors were docked at the active site of CK2 (PDB id: 2ZJW) using AutoDock software, which resulted in energy-based descriptors such as binding energy, intermol energy, torsional energy, internal energy and docking energy. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using energy-based descriptors yielding correlation coefficient r(2) of 0.4645. To assess the predictive performance of QSAR models, different cross-validation procedures were adopted. Our results suggests that ligand-receptor binding interactions for CK2 employing QSAR modeling seems to be a promising approach for prediction of IC50 value of a new ligand molecule against CK2.Further, twenty analogues of ellagic acid were docked with CK2 structure. After docking, two compounds CID 46229200 and CID 10003463 had lower docking energy even lower than standard control Ellagic acid with CK2 was selected as potent candidate drugs for Oral cancer. The biological activity of two compounds in terms of IC50 was predicted based on QSAR model, which could be used as a guideline for anticancerous activity of compounds before their synthesis.
Keywords: AutoDock; CK2; Docking; Ellagic acid; Ellagic acid analogues; Protein kinase.