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• W3032285440 endingPage "4110" @default.
• W3032285440 startingPage "4100" @default.
• W3032285440 abstract "γ-aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5′-phosphate (PLP)-dependent enzyme which degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target which regulates the GABA level. Novel and potent drug development to inhibit GABA-AT is still very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. However, the human GABA-AT crystal structure is not available yet, and we built the 3D structure of human GABA-AT based on the crystal structure of pig’s liver (Sus Scrofa) enzyme as a template. The generated model was validated with numerous tools such as ProSA and PROCHECK. A set of selected well-known inhibitors have been tested against the modeled GABA-AT. Molecular docking studies have been accomplished via application of Genetic Optimization for Ligand Docking (GOLD), Vina and Autodock 4.2 software to search for potent inhibitors. The best two candidate inhibitors have been computationally examined for absorption, distribution, metabolism, elimination and toxicity descriptors (ADMET) and Lipinski’s rule of 5. Lastly, molecular dynamics (MD) simulations were carried out to inspect the ligands’ binding mode and stability of the active site of human GABA-AT over time. The top ranked ligands exhibited reliable stability throughout the MD simulation. The selected compounds are promising candidates and might be tested experimentally for the inhibition of human GABA-AT enzyme. Communicated by Ramaswamy H. Sarma" @default.
• W3032285440 created "2020-06-05" @default.
• W3032285440 creator A5028835825 @default.
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• W3032285440 date "2020-06-08" @default.
• W3032285440 modified "2023-10-16" @default.
• W3032285440 title "Homology modeling of human GABA-AT and devise some novel and potent inhibitors via computer-aided drug design techniques" @default.
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• W3032285440 doi "https://doi.org/10.1080/07391102.2020.1774417" @default.
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