Amyloid deposits characterize many diseases, including particularly neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s diseases, in addition to Type II diabetes, each associated with the aggregation of a distinct protein. A major step of amyloid formation is growth of amyloid fibrils by misfolded or unstructured proteins, involving protein binding and structural transitions. The structural details of the transitions hold the key to understanding the process but remain poorly understood. Computational characterization of the structural transitions is limited owing to the long timescale of the elongation processes. In this talk, I will focus on our computational efforts in characterizing amyloid growth by amyloid-β peptides that are linked closely to Alzheimer’s Diseases. I will discuss the methodological framework employed in our studies, with a particular emphasis on the importance of combining both multiscale modeling and enhanced sampling to overcome the limit of computational timescale required for simulating structural transitions during amyloid growth. I will also show usefulness of representing the structural transitions as a network of kinetic equations and demonstrate that on the basis of the network key thermodynamic and kinetic parameters pertaining to the amyloid growth can be derived. With these parameters, many critical mechanistic questions regarding the amyloid growth can be addressed, such as what are detailed transition pathways, how important particular structures of protein monomers are for the growth process, and why some amyloids grow unidirectionally. Moreover, I will discuss also our recent effort in employing the network model to investigate amyloid inhibition. Our study reveals the potential impact of structural disorder at amyloid end on amyloid inhibition, providing insights into design of amyloid inhibitors.

Dr. Han's research interest lies at the interface between chemistry, biology, and computational science. He is particularly interested in elucidating the atomic-level mechanisms of protein structure changes arising in several processes essential for cellular functions, such as protein-protein interactions and protein aggregation. He develops and applies cutting-edge computational simulation methods to elucidate the dynamical changes of proteins during these processes. His goal is to provide, through the computer simulations, deeper insights into the molecular basis of protein function and malfunction, assisting experimentalists in creating novel approaches to modulate protein conformational dynamics for biomedical applications.