γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5'-phosphate (PLP)-dependent enzyme that 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 that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.
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http://dx.doi.org/10.3390/ijms242316990 | DOI Listing |
J Phys Chem B
January 2025
School of Chemistry, The University of New South Wales, Sydney, NSW 2052, Australia.
A systematic series of QM cluster models has been developed to predict the trend in the carbonic anhydrase binding affinity of a structurally diverse dataset of ligands. Reference DLPNO-CCSD(T)/CBS binding energies were generated for a cluster model and used to evaluate the performance of contemporary density functional theory methods, including Grimme's "3c" DFT composite methods (rSCAN-3c and ωB97X-3c). It is demonstrated that when validated QM methods are used, the predictive power of the cluster models improves systematically with the size of the cluster models.
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Department of Biotechnology, Kakatiya University, Warangal, Telangana, India.
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Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, Dhaula Kuan, New Delhi, 110021, India.
Nanobodies or variable antigen-binding domains (VH) derived from heavy chain-only antibodies (HcAb) occurring in the Camelidae family offer certain superior physicochemical characteristics like enhanced stability, solubility, and low immunogenicity compared to conventional antibodies. Their efficient antigen-binding capabilities make them a preferred choice for next-generation small biologics. In the present work, we design an anti-SARS-CoV-2 bi-paratopic nanobody drug conjugate by screening a nanobody database.
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January 2025
Department of Urology Surgery, The Second Affiliated Hospital of Guilin Medical University, Guilin, 541199, Guangxi, China.
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