Calculate β-hairpin folding trajectories of a confined protein, a computational challenge previously thought to require "big iron."
Protein folding is the process by which strings of amino acids (proteins) take on three-dimensional shapes that determine their function. Folding in cells occurs in the presence of lipids, carbohydrates, and other biological molecules, i.e., in a crowded environment. Therefore, many proteins spontaneously fold to their native states in the environment, which geometrically restricts their conformational space. Various forms of cancer in addition to diseases such as Alzheimer's, cystic fibrosis and sickle cell anemia are formed when a defect occurs in the folding process. Understanding the complex nature of this behavior may give researchers the vital information they need to determine how to combat these diseases.
Some proteins also have natural tendencies to prevent or fight disease. For example, the P53 "Tumor Suppressor" protein is known to be involved in important metabolic pathways for the body's destruction of precancerous cells. Understanding the structure of proteins like P53 can be crucial to discovering new cancer treatments, and to finding ways to prevent cancer altogether.
Traditional computer simulations are most often performed on clusters of workstations or by using supercomputers. In both cases, the number of available CPUs is typically limited versus the number that can be supplied via Parabon's Computation on Demand® service.
Dr. Devarajan "Dave" Thirumalai, a University of Maryland professor and internationally recognized chemist, spearheaded a protein folding study in which he studied the thermodynamics and kinetics of an off-lattice Go model hairpin from an Ig-binding protein confined to a spherical pore. His research team validated the ability to improve the speed of their research by using Parabon's Computation on Demand service for calculations that previously required a large cluster of high-powered workstations and the work was ultimately published in the Proceedings of the National Academy of Sciences.1
"With this technology, these computations can be sped up considerably allowing our team to analyze more complex data at a faster rate achieving success much more quickly," said Dr. Thirumalai. "There is great promise that significant progress can be made in understanding the assembly of proteins and RNA with complex architectures."
1 Klimov DK, Newfield D, Thirumalai D (2002). Simulations of beta-hairpin folding confined to spherical pores using distributed computing. Proceedings of the National Academy of Science, USA 2002 Jun 11 99(12):8019-24. http://www.pnas.org/cgi/content/abstract/99/12/8019