| Summary: | 碩士 === 國立臺北科技大學 === 生化與生醫工程研究所 === 101 === Alzheimer''s disease (AD) is the most common progressive chronic neurodegenerative disorder characterized by loss of neurones particularly in those regions associated with cognitive functions and cortical atrophy. Neuropathological hallmarks include neurofibrillary tangles (NFTs) and amyloid-beta plaques. To date, no truly effective therapy drugs has been developed for AD. Previous studies show that tau protein and beta-secretase (BACE1) are two predominant targets for anti-AD drugs. In this study, we applied many computational approaches including pharmacophore modeling, 3D-QSAR modeling, molecular docking, molecular dynamics (MD) simulations and virtual screening to discover more potential anti-AD drugs. For tau protein, we constructed structure-based pharmacophore model that was developed using the representative docked conformations from the most effective peptide inhibitors. This model was subsequently used as a 3D-query in virtual screening to identify potential hits from Traditional Chinese Medicine (TCM) database. The binding stabilities of these hits were further validated using molecular dynamics simulations. Finally, only three compounds were identified as potential leads, which exhibited similar binding affinities in comparison to the most effective peptide inhibitor for tau protein. As to BACE1, we constructed multicomplex-based pharmacophore model by a collection of 9 crystal structures of BACE1-inhibitor complex. This model was validated by Guner-Henry (GH) scoring methods, applied to screen the TCM database and and to align the structurally diverse BACE1 inhibitors. Then, 3D-QSAR analysis and molecular docking were conducted to retrieve two potential lead compounds. In summary, the results of this study can be applied to the design of new and more potent anti-AD drugs for clinical purposes.
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