The research in the Bevan Lab involves the application of computational molecular modeling to relate the structure and dynamics of molecular systems to function. Systems currently under investigation include the amyloid β-peptide that is associated with Alzheimer's disease and peroxisome proliferator-activated receptor that is associated with inflammation, diabetes, and obesity. We also are initiating projects involving G-protein coupled receptors (GPCRs), and irisin, a recently discovered protein with hormone-like properties. Finally, we are using computational methods to design enzymes, with our strategy being to alter the substrate specificity of existing enzymes.
Amyloid β-Peptide (Aβ)
The aggregation of Aβ, ultimately forming plaques, is associated with the development of Alzheimer's disease. We have examined the physicochemical parameters that account for stability of the Aβ aggregates, and we have proposed a mechanism by which a certain class of compounds, the flavonoids, may prevent the aggregation of Aβ into the toxic oligomeric species. We also have performed simulations of Aβ associated with lipid bilayers because disruption of membrane integrity is a proposed mechanism for toxicity of Aβ. We have published 10 papers on this topic over the past 7 years.
Peroxisome Proliferator Activated Receptor (PPAR)
The number of Americans with chronic inflammation-related diseases, including diabetes and obesity, rises significantly each year. The therapeutics used to treat type 2 diabetes, thiazolidinediones (TZDs), often exacerbate the problem by causing side effects, like edema and heart complications. In an attempt to discover more effective and less harmful treatments, our group is working on a combined computational and experimental approach for finding PPARγ, PPARδ, and PPARα agonists. This project involves collaboration with Dr. Josep Bassanganya-Riera of the Virginia Bioinformatics Institute, who guides the wet lab studies with which we correlate our computational studies. The computational work has been carried out primarily by Nikki Lewis, a former graduate student in the GBCB program, who was supported for two years by the Initiative to Maximize Student Development (IMSD) program and then by an NIH F31 grant.
GPCRs and Irisin
GPCRs are the targets for approximately 30% of the drugs that are currently being marketed and thus are important in a variety of diseases. Our interests are primarily in characterizing those GPCRs that are activated by fatty acids. These compounds also activate PPAR-gamma and some of these compounds have the potential to act through both of these receptors, which adds to the complexity when attempting to understand mechanisms of action of these compounds. Our modeling approaches will aid in elucidating the relationship among structure, dynamics, and function.
Irisin is a recently discovered protein hormone that is associated with obesity. It has been shown to stimulate the conversion of white fat cells to brown fat cells, which are much more metabolically active. Very little information is available related to irisin structure, and we are using its sequence relationship to other proteins to predict its structure. We will then apply MD to refine these structures and examine their stability. These projects are being done in collaboration with Dr. Bin Xu in the Department of Biochemistry.
Enzymes as Biocatalysts
We have recently initiated a collaborative project with Dr. Ryan Senger of the Department of Biological Systems Engineering. Dr. Senger has developed an approach to identify metabolic pathways that may lead to the production of novel biofuels and other chemical intermediates using bacterial systems. These systems have the potential to be very specific for the products they produce and environmentally friendly. One of the challenges is that the enzymes that are involved in these pathways may have low activity towards the substrates on which they need to act. We will apply molecular modeling approaches to model structures of the enzymes, examine their dynamics, dock potential substrates, and predict changes in the enzyme that may improve activity towards the substrates.