NIMA related Kinases (NEK7) plays an important role in spindle assembly and mitotic division of the cell. Over expression of NEK7 leads to the progression of different cancers and associated malignancies. It is becoming the next wave of targets for the development of selective and potent anti-cancerous agents. The current study is the first comprehensive computational approach to identify potent inhibitors of NEK7 protein. For this purpose, previously identified anti-inflammatory compound i.e., Phenylcarbamoylpiperidine-1,2,4-triazole amide derivatives by our own group were selected for their anti-cancer potential via detailed Computational studies. Initially, the density functional theory (DFT) calculations were carried out using Gaussian 09 software which provided information about the compounds' stability and reactivity. Furthermore, Autodock suite and Molecular Operating Environment (MOE) software’s were used to dock the ligand database into the active pocket of the NEK7 protein. Both software performances were compared in terms of sampling power and scoring power. During the analysis, Autodock results were found to be more reproducible, implying that this software outperforms the MOE. The majority of the compounds, including M7, and M12 showed excellent binding energies and formed stable protein–ligand complexes with docking scores of − 29.66 kJ/mol and − 31.38 kJ/mol, respectively. The results were validated by molecular dynamics simulation studies where the stability and conformational transformation of the best protein–ligand complex were justified on the basis of RMSD and RMSF trajectory analysis. The drug likeness properties and toxicity profile of all compounds were determined by ADMETlab 2.0. Furthermore, the anticancer potential of the potent compounds were confirmed by cell viability (MTT) assay. This study suggested that selected compounds can be further investigated at molecular level and evaluated for cancer treatment and associated malignancies.
Structurally diverse adamantyl-iminothiazolidinone conjugates were synthesized, evaluated for elastase inhibition, and subjected to in silico ADMET prediction. The inhibition studies revealed compounds 5a, 5f, 5g, and 5h to show significant activity.
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.
NEK7 plays a crucial role in many signaling pathways and contributes to a variety of cancers. Therefore, NEK7 has long been considered an attractive drug target in anti-cancer drug discovery. However, only a few efforts have been made in development of NEK7 inhibitors with selectivity. In the present study, we have investigated FDA approved kinase inhibitors as potential NEK7 inhibitors. Although more than 200 FDA approved drugs are available but none is known to inhibit NEK7 protein. These ndings motivated us to design in-silico approach for investigation and identi cation of NEK7 protein. In the current study, Structure-based virtual screening and molecular docking were carried out to identify potential NEK7 inhibitors. Dacomitinib and Neratinib was selected depending upon their potential activities against various cancer cell lines. These drugs were subjected to density functional theory calculations which demonstrated the chemical reactivity of both drugs. Furthermore, molecular docking studies were conducted using Molecular Operating environment 2015.10 and conformations with high docking scores and strong interactions were selected for further analysis. Absorption, distribution, metabolism, elimination and toxicity (ADMET) pro le evaluation was also carried out to ascertain toxicity pro le of both drugs. The proposed inhibitorprotein complexes were further subjected to Molecular Dynamics (MD) Simulations studies involving root-mean-square deviation and root-mean-square uctuation to explore the binding mode stability inside active pockets. Finally, both drugs were obtained as potential inhibitors of NEK7 protein. All these analyses provide a reference for the further development of NEk7 inhibitors.
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