The cause of Alzheimer’s disease is not yet identified and there is currently neither an understanding of the pathogenesis of the disease nor a rational strategy towards a treatment. It is clear that deposits in the brain consisting of small peptides identified as beta-amyloids play some role in the destruction of brain cells that ultimately leads to dementia and complete loss of brain function. One of the leading hypothesis is that the beta-amyloid deposits bind copper, which then catalyzes the generation of hydrogen peroxide from dioxygen, which causes oxidative damage. Unfortunately, the structure of these Cu-amyloid deposits are not available and nobody knows how copper binds to these peptides.
IU Professor, Mu-Hyun “Mookie” Baik and coworkers have used TeraGrid resources, the NSF-funded Data Capacitor and the IU’s Big Red supercomputer to construct a large scale computer model of these amyloid entities and examine the copper binding behavior. For the first time, a high-resolution structure of the copper bound beta-amyloid was obtained and communicated in a recent publication in the Journal of Biological Inorganic Chemistry in fall of 2008.
In addition to providing structural information, Baik found that the copper binding behavior is highly dependent on the length of the peptide oligomer. The biologically relevant amyloid is a 42-residue paptide, which is not very soluble. For experimental convenience many researchers use a close analogue, a 40-residue peptide, as a model to the biologically important amyloid. Baik showed for the first time that the copper bound structures of these two analogous peptides are dramatically different, providing a possible explanation for the dramatically different results and controversies reported in the literature, where peptides of different lengths were used as models. Baik’s structural work provides a foundation for future work within Baik’s and other research groups to address structural and reactivity inquiries. Baik’s recently released results will fundamentally change the way medical researchers further study Alzheimer’s disease and move toward the development of more effective treatments. Baik’s potentially life-saving work could not have been accomplished without access to NSF-funded technology resources.
Mu-Hyun Baik - http://info.chem.indiana.edu/sb/page/normal/757.html
NSF GSS Codes:
Primary Field: Clinical Medicine (717) - Neurology
Secondary Field: Computer Science (401) - Data Modeling