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New algorithms aid in disease research
The study of biomolecular interactions, particularly involving protein receptors, has grown increasingly important in recent years.
Scientists believe such work holds promise in the development of targeted drugs and the eventual cure of disease. Researchers in the Computer and Information Technology Institute (CITI) have partnered with colleagues at the University of Texas Health Science Center-Houston to develop new algorithms to help scientists study biomolecular interactions.
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| Graduate student Mili Shah and her adviser, Danny Sorensen, use the Cray SD1 supercomputer for their research. |
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The interactions under study occur when a ligand (atom, ion or molecule) attaches to another molecule (usually a larger protein) known as a receptor.
“We are looking for molecules that can block the docking sites found on disease-related molecules,” said Mili Shah, a graduate student in the Department of Computational and Applied Mathematics (CAAM).
Proteins are made of hundreds or thousands of atoms, and may assume different forms and functions but are limited in how they can be altered. Researchers need to gain a deeper understanding of how proteins behave, and in particular how specific ligands bind to receptors.
The research team, led by Danny Sorensen, the Noah Harding Professor of Computational and Applied Mathematics, focuses on new and promising computational algorithms to isolate protein characteristics faster and more effectively. “These algorithms will ultimately help reduce the time it takes to analyze proteins from days and months to minutes and hours using resources such as our Cray XD1 supercomputer,” Sorensen said.
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