Protein aggregation, hydrophobicity and neurodegenerative disease

In this thesis, we study the relationship between proteins and neurodegenerative disease using different computational and experimental approaches. We focus on two disease mechanisms: changes in protein abundance and changes in protein structure. Additionally, we investigate the difference in unfolding and refolding profile between native and denatured proteins. Changes in protein structure are especially important in neurodegenerative disease, where large β-stranded aggregates called amyloid fibrils are thought to play a major role. Some amyloid fibrils are known to denature at low temperatures. In Chapter 2, we delineate the entropic and enthalpic contributions of the temperature dependence of hydrophobicity in relation to cold denaturation. We use Monte Carlo simulations on a 3D lattice to gain mechanistic insight into cold denaturation of amyloid fibrils. Additionally, we confirm our results using isothermal titration calorimetry experiments on different types of amyloid fibrils in vitro. We identify three necessary conditions for cold denaturation of amyloid fibrils. We conclude that while heat denaturation of amyloid fibrils is mostly driven by the chain entropy, cold denaturation of fibrils is mostly driven by the hydrophobic contribution to the enthalpy. In Chapter 3, we define three measures for protein surface hydrophobicity and develop a tool to calculate surface hydrophobicity from 3D protein structure. Additionally, we try to predict these measures from sequence and use these predictions to investigate the abundance of protein surface hydrophobicity in the human proteome. We find that proteins in the human brain are relatively hydrophobic, which could explain why protein aggregation diseases are mostly found in the nervous system. In Chapter 4, we use a combination of Quartz Crystal Microbalance experimental measurements with kinetic models to study the effect of disordered flanks on fibril formation of α-synuclein. We find that these flanks have an inhibitory effect on amyloid fibril formation. Without the flanks, amyloid fibril growth was significantly increased. The mathematical models suggest that the increased growth rate is due to secondary nucleation, a process by which monomers bind to the amyloid fibril surface, eventually leading to the growth of new fibrils away from the surface of the initial fibril. In Chapter 5, we investigate the interaction between nanoPET and α-synuclein. Nanoplastics are commonly found as pollutants in the environment. We show that nanoPET may be able to enhance amyloid fibril formation of α-synuclein, and thus exposure to these compounds might stimulate amyloidoses. Because misfolded proteins can play a role in disease, it would be valuable to be able to distinguish native from misfolded proteins in protein samples. In Chapter 6, we combine steered MD simulations with AFM experiments to study the difference in unfolding pattern between native and denatured Hemoglobin protease (Hbp) proteins at single-molecule level. Additionally, we show that the stability of Hbp comes from a stack of hydrophobic residues in the core of the protein, and study the refolding ability of Hbp after being unfolded. In Chapter 7, we use a proteomics workflow to investigate the molecular pathways affected in frontotemporal dementia, including a novel method to validate the results. We identify and validate several modules of co-abundant proteins affected in FTLD-tau. Specifically, PTBP1 is a protein that plays an important role in the alternative splicing of MAPT, and could be an interesting therapeutic target for diagnosis or treatment of FTD. In conclusion, we have studied the relationship between protein aggregation, hydrophobicity and neurodegenerative disease. In this thesis, we use different experimental and computational techniques to delineate physical factors affecting amyloid fibril formation and growth. We identify hydrophobicity as one of the major physical properties important for aggregation. Additionally, we identify molecular pathways affected in frontotemporal dementia through a novel proteomics workflow.