Inhibitor search and variant analysis of Acetylcholinesterase
- Authors: Ras, Harnaud
- Date: 2021-04
- Subjects: Acetylcholinesterase , Alzheimer's disease , Acetylcholinesterase -- Inhibitors , Alzheimer's disease -- Chemotherapy , Cerebrovascular disease -- Treatment , Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA)
- Language: English
- Type: thesis , text , Masters , MSc
- Identifier: http://hdl.handle.net/10962/178191 , vital:42919
- Description: Acetylcholinesterase (AChE) inhibition is used to treat Alzheimer's disease by increasing the availability of acetylcholine to carry nerve signals in the brain. The response to this treatment varies widely, which may be due to altered affnity to the current drugs caused by genetic variation. Various negative side-effects limit their use. As this is one of the only available therapeutic drug targets to treat Alzheimer's disease, decreasing the negative effects is of great importance. AChE is involved in biological processes that occur after acute ischemic stroke. Stroke is the third leading cause of death worldwide, and 87% of all stroke cases belong to ischemic stroke. AchEI (cholinesterase inhibitors) have been suggested to have properties that lower the risk of stroke. AChE is one of 15 verified drug targets under study for treatment of stroke. In addition to Alzheimer's disease and stroke, Lewy body disease (LBD) may be treated using cholinesterase inhibitors. The goals of this study are to find inhibitors that can potentially be used to treat Alzheimer's disease and/or stroke and to investigate variants which may affect protein dynamics and function. Two variants were analyzed, P247L and T229S. Molecular simulation of the P247L variant resulted in a disruption in protein dynamics in comparison to the wildtype. A total of 5728 molecules were screened and 10 nanosecond simulations were used to narrow down the set of compounds. The four best performing molecules were simulated for 10 nanoseconds. MM-PBSA was performed to identify molecules with high binding free energies. , Thesis (MSc) -- Faculty of Science, Biochemistry and Microbiology, 2021
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- Date Issued: 2021-04
Molecular simulations of potential agents and targets of Alzheimer’s disease
- Authors: Joli, Luxolo
- Date: 2020
- Subjects: Alzheimer's disease -- Chemotherapy , Alzheimer's disease -- Treatment , Ligands (Biochemistry) , Proteins -- Chemistry , Molecular dynamics -- Simulation methods
- Language: English
- Type: text , Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/146411 , vital:38523
- Description: Alzheimer's Disease (AD) is a neurodegenerative brain disorder that was first discovered in 1901 by Dr Aloïs Alzheimer and was later reported publicly in 1906. The German doctor had a 51-yearold woman patient called Auguste Deter, who was suffering from a rare brain disorder with early signs of memory loss and cognition. Alzheimer's Disease is the most common type of dementia that affects people with the age of 65 years and older. There is no single known cause of Alzheimer’s disease however, amyloid β-peptide (Aβ1–40/42) was found to be at the centre of AD pathogenesis and this connection was referred as “amyloid hypothesis”. It is suspected that an accumulation of amyloid β-peptide is a major contributor to neuronal dysfunction and degeneration. Alzheimer’s disease is complex and therefore, currently there is no medication available that treats the disease. However, there are approaches that focus on helping people maintain mental function, manage behavioral symptoms, and slow down the symptoms of disease. According to South Africa’s 2011 census, there are approximately 2.2 million people in South Africa with some form of dementia and therefore there is a need to find a treatment for the disease. This study aims to find agents and targets of Alzheimer’s Disease by using different computational techniques such as molecular modelling. The study will use compounds from the South African Compounds Database (SANCDB) and the following therapeutic targets α-, β- and γ-secretase, acetylcholinesterase, tau protein and neprilysin. A successful High-throughput Virtual Screening (HTVS) study to determine lead compounds was performed using a computational program called KNIME. Molecular docking was achieved with GLIDE as it allows for exhaustive ligand flexibility. The docking calculations were carried out using the high level of precision XP (extra precision) for enhanced docking accuracy. The binding affinities (docking scores) for the best bound ligands obtained from docking were in the order of -5 kcal/mol or less. The ligandSANC00370 was the best binding ligand against the protein 1J1C_B and had the best binding energy of -13.94 kcal/mol compared to others. The receptor-ligand complexes were analyzed using the interaction diagrams obtained from the Discovery Studio Visualizer and Maestro programs. Molecular Dynamics simulations were performed on the complexes obtained from docking to help in optimizing their interactions. The simulations were performed using the Desmond tool with the OPLS3 force field. 100 ns simulations were performed for six systems with the best docking score results epresenting each of the therapeutic targets and for the other complex systems, 50 ns simulations were performed. The Desmond simulations were analyzed using the Simulations Interaction Diagrams such as PL-RMSD, L-RMSF, P-RMSF, L-Torsions, P-SSE, LP-Contacts and L-Properties. Maestro was used to visualize the stability of the ligands in the active site during the simulation. All 13 Desmond simulations were successful however, there were 9 simulations which produced satisfactory results while the others were nsatisfactory. Based on the molecular docking and Molecular Dynamics results of this study, 9 potential targets and 6 potential agents were obtained successfully and can be studied further as therapeutics for Alzheimer’s Disease.
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- Date Issued: 2020
Structure and interaction studies of beta-amyloid in the search for new lead compounds for the treatment of Alzheimer’s disease
- Authors: Mtini, Onke
- Date: 2020
- Subjects: Alzheimer's disease -- Chemotherapy , Alzheimer's disease -- Treatment , Amyloid beta-protein , Oxidative stress , Protein binding , South African Natural Compounds Database
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10962/167574 , vital:41493
- Description: Alzheimer’s disease (AD) is the most devastating neurodegenerative disorder that effects the aging population worldwide. In this study three hypotheses of AD are explored, the β-amyloid cascade hypothesis, the β-amyloid metal binding hypothesis and the oxidative stress hypothesis are explored. In the first case compounds from the South African Natural Compounds Database (SANCDB) are docked to models of β-amyloid fibrils and the properties of these fibrils under pulling simulations are compared to a known small molecule disruptor of β-amyloid, wgx-50. In these simulations SANCDB compounds are identified that disrupt β-amyloid in a similar manner to wgx-50. In these simulations the disruption to the free energy of binding of chains to the fibrils is quantified. For metal binding and oxidative stress hypotheses, problems in simulation arise due to only fragments of β-amyloid being present in the Research Collaboratory for Structural Bioinformatics protein data bank (RCSB PDB), as determined from NMR experiments. In this work, β-amyloid is set up under periodic boundary conditions to simulate a fibril under reasonable computational time. Within these periodic boundary conditions, β-amyloid has been solvated in copper and zinc rich environments and diffusion of these metals around the fibrils has been explored. The localization of these metals (in simulation only using van der Waal’s and electrostatic terms) around the fibril has led us to explore other possible metal binding sites. Metal bound to the infinite fibril has been optimized at the QM/MM level and some of the reactive oxygen species in the presence of the fibril are quantified.
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- Date Issued: 2020