ABSTRACT A comprehensive understanding of the complex biological processes associated with Alzheimer's Disease (AD) in mammalian systems requires their molecular characterization at the systems level. Existing methods for such systems level analyses have typically involved the measurement of gene and protein expression levels. Unfortunately, while the application of such gene and protein expression level analyses to the characterization of AD has identified a few biomarkers of the disease, the practical utility of these markers has been limited, especially for the development of drug therapies. Thus, there is a need for additional research tools to better understand AD pathogenesis and uncover more useful AD biomarkers. Proposed here is an effort to investigate the use of large-scale protein folding stability measurements at the systems level to characterize AD. In contrast to gene and protein expression level analyses, the protein folding stability analyses proposed here are expected to report more directly on biologically significant phenomena generally postulated to be responsible for AD pathogenesis such as the mutation, modification, and misfolding of proteins. The proposed work will investigate the use of three mass spectrometry-based methods for making protein folding stability measurements on the proteomic (including the Stability of Proteins from Rates of Oxidation (SPROX) technique, the Thermal Proteome Profiling (TPP) technique, and the Limited Proteolysis technique (LiP) to identify proteins with AD-related changes in their folding stability using a transgenic mouse model of the disease (5XFAD). The specific aims of this work are: (1) to generate protein folding stability profiles on mouse brain proteins derived from the hippocampus region of 5XFAD and control B6SJLF1/J mice aged 2 and 8 months using 10 mice in each cohort; (2) to identify “hit” proteins with differential folding stabilities in the age-matched 5XFAD and control B6SJLF1/J mice; and (3) to characterize the “hit” proteins identified in (2) using bioinformatics tools and biochemical assays to better understand their AD-related functions and disysfunctions.