| 1. |
- Aziz, Mubashir, et al.
(author)
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Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7 : A Novel Target for the Treatment of Cancer
- 2022
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In: Molecules. - : MDPI. - 1431-5157 .- 1420-3049. ; 27:13
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Journal article (peer-reviewed)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.
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| 2. |
- Ejaz, Syeda Abida, et al.
(author)
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New Insight into the Pharmacological Importance of Atropine as the Potential Inhibitor of AKR1B1 via Detailed Computational Investigations: DFTs, ADMET, Molecular Docking, and Molecular Dynamics Studies
- 2023
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In: Applied Biochemistry and Biotechnology. - : SPRINGER. - 0273-2289 .- 1559-0291. ; 195:8, s. 5136-5157
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Journal article (peer-reviewed)abstract
- The aim of this research is to investigate the quantum geometric properties and chemical reactivity of atropine, a pharmaceutically active tropane alkaloid. Using density functional theory (DFT) computations with the B3LYP/SVP functional theory basis set, the most stable geometry of atropine was determined. Additionally, a variety of energetic molecular parameters were calculated, such as the optimized energy, atomic charges, dipole moment, frontier molecular orbital energies, HOMO-LUMO energy gap, molecular electrostatic potential, chemical reactivity descriptors, and molecular polarizability. To determine atropines inhibitory potential, molecular docking was used to analyze ligand interactions within the active pockets of aldo-keto reductase (AKR1B1 and AKR1B10). The results of these studies showed that atropine has greater inhibitory action against AKR1B1 than AKR1B10, which was further validated through molecular dynamic simulations by analyzing root mean square deviation (RMSD) and root mean square fluctuations (RMSF). The results of the molecular docking simulation were supplemented with simulation data, and the ADMET characteristics were also determined to predict the drug likeness of a potential compound. In conclusion, the research suggests that atropine has potential as an inhibitor of AKR1B1 and could be used as a parent compound for the synthesis of more potent leads for the treatment of colon cancer associated with the sudden expression of AKR1B1.
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| 3. |
- Ofoegbu, Obinna, et al.
(author)
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Molecularly Imprinted Chitosan-Based Thin Films with Selectivity for Nicotine Derivatives for Application as a Bio-Sensor and Filter
- 2021
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In: Polymers. - : MDPI. - 2073-4360. ; 13:19
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Journal article (peer-reviewed)abstract
- This study reports the feasible use of chitosan as a thin film biosensor on the very sensitive quartz crystal micro balance system for detection of blends of multiple templates within a single matrix. The development of chitosan-based thin film materials with selectivity for nicotine derivatives is described. The molecular imprinting of a combination of nicotine derivatives in N-diacryloyl pipiradine-chitosan-methacrylic acid copolymer films on quartz crystal resonators was used to generate thin films with selectivity for nicotine and a range of nicotine analogues, particularly 3-phenylpyridine. The polymers were characterized by spectroscopic and microscopic evaluations; surface area, pore size, pore volume using Breuner-Emmet-Teller method. Temperature characteristics were also studied. The swelling and structure consistency of the Chitosan was achieved by grafting with methylmethacrylic acid and cross-linking with N-diacrylol pipiradine. A blend of 0.002 g (0.04 mmol) of Chitosan, 8.5 μL Methylmethacrylic Acid and 1.0 mg N-diacrylol pipradine (BAP) presented the best blend formulation. Detections were made within a time interval of 99 s, and blend templates were detected at a concentration of 0.5 mM from the Quartz crystal microbalance resonator analysis. The successful crosslinking of the biopolymers ensured successful control of the swelling and agglomeration of the chitosan, giving it the utility potential for use as thin film sensor. This successful crosslinking also created successful dual multiple templating on the chitosan matrix, even for aerosolized templates. The products can be used in environments with temperature ranges between 60 °C and 250 °C.
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