| KEYWORD |
Area Engineering
Molecular modeling of APP protein mutations aimed at its major role in both neuroprotection and AD pathology
keywords ALZHEIMER'S DISEASE, APP PROTEIN, DOCKING, GENETIC MUTATIONS, MOLECULAR MODELING AND SIMULATION, SECRETASE ENZYME
Reference persons JACEK ADAM TUSZYNSKI
External reference persons Dr. Lorenzo Pallante
Research Groups 28- biomedica
Thesis type COMPUTATIONAL
Description Alzheimer's disease is the most common form of dementia among the elderly, with a prevalence of 5% in people 65 or older. While a number of treatments have been studied, currently there is no therapeutic intervention available that would reverse the course of the disease. A major reason for this lack of successful therapeutic advances is the fact that the biochemistry of Alzheimer's disease (AD) is still unclear. Until now, the main hypotheses involving the initiation and progression of AD focused on the amyloid and tau proteins which result in the production of extracellular beta amyloid plaques and aberrant cytoplasmic tau neurofibrillary tangles. These observations are correlated with neuronal death and memory loss. However, in 2012, Jonsson et al. discovered the A673T variant in the Amyloid Precursor Protein (APP) which was subsequently understood as a protective mutation, correlated with healthy brain aging and an inferior beta-amyloid peptide production shown to be up to 40% in-vitro. The molecular mechanism behind this effect is expected to be a difference in the interaction of APP with the beta secretase protein. On the other hand, other authors highlight the different structural and functional properties of the mutated beta amyloid peptides. This project would start from this observation where the first known protective mutation in AD is identified and we will explore this and several different mutations by using computational modeling in order to gain insights in to their role in the molecular interactions with secretase enzymes. A particular focus of this study will be on the crucial sites (position 670-671-672-673) of APP. We will use MOE software and molecular dynamics packages such as GROMACS in our simulations, as well as AI-based tools such as alphafold.
Required skills Basic knowledge of bioinformatics; familiarity with MOE computational software, molecular dynamics
Deadline 18/05/2025
PROPONI LA TUA CANDIDATURA