CAREER: Combining Research and Education via the Exploration of Peptide-Lipid Binding and Aggregation

University Of Maryland, College Park, College Park MD

Investigators

Abstract

With this award, the Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Silvina Matysiak from The University of Maryland, College Park to investigate how lipid composition affects peptide self-assembly pathways on a lipid membrane. The project involves the use of computational methods to establish how the chemistry of a lipid headgroup promotes peptide recruitment, misfolding and aggregation in the cases of several amyloidogenic peptides. These studies will provide fundamental information on vital conditions that promote or impede peptide self-assembly and that are responsible for the morphology of the resulting aggregates. The results of this project may be used to understand how protein misfolding in neurons may lead to neuronal dysfunction. As part of the project, several toolboxes will be created to teach parents and students at the high school and undergraduate levels, the importance of molecular interactions in the everyday world. In order to achieve this goal, real-time videos of living cells or animations will be combined with interactive hands-on modeling of proteins. There is a fundamental gap in understanding how proteins misfold and aggregate on cellular membranes. This project involves a computational investigation that aims at elucidating how membrane composition modulates amyloid peptide aggregation. A deeper understanding of the driving forces behind peptide aggregation on membranous surfaces would have impact on a number of disciplines ranging from biomaterials to biotechnology. The approaches involve an expansion of a previously developed coarse-grained model in the context of this project. The project aims to 1) study the role of peptide-lipid hydrogen bonds in peptide aggregation, 2) quantify the impact of electrostatic interactions in shaping aggregation pathways, 3) dissect the influence of multivalent ions in lipid domain formations and how this affects peptide energy landscapes and 4) characterize how changes in a membrane's physical properties can modify peptide aggregation behavior. The computational models developed in this award will be made freely available to the public.