Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

Abstract

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.

Design, synthesis, biochemical and in silico characterization of novel naphthalene-thiourea conjugates as potential and selective inhibitors of alkaline phosphatase

“…After completing the parameters related to protein, the ligand library was prepared by undergoing preliminary energy minimization protocol in Chem3D Pro 12.0 [ 23 ] and saved in a desired format for docking. A grid box of 1 angstroms extended 23.33500, 20.72500 and 18.524000 angstroms in X, Y, and Z dimensions was generated to facilitate molecular docking using AutoDock Vina [ 25 ]. The Lamarckian algorithm generated 50 poses for each conformation, and the top-ranked pose of the compound was further analyzed using PyMOL [ 26 ] and LigPlot plus software [ 27 ].…”

Anticancer Potential of Sulfonamide Moieties via In-Vitro and In-Silico Approaches: Comparative Investigations for Future Drug Development

“…This method determines the binding free energy by computing the difference between the free energies of the protein-ligand complex and the unbound components (i.e., the protein and the ligand). In MMGBSA energy analysis, the free-binding energies of protein, ligand, and the protein-ligand complex are estimated by the following equation [30]:…”

Spectroscopic, Thermodynamic and Molecular Docking Studies on Molecular Mechanisms of Drug Binding to Proteins

“…A few medications have recently been developed to target the NEK7-mediated inflammasome pathway, but the mechanism and treatment outcomes are not specific and consistent. Aziz et al [ 12 ] focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina.…”