Center for Computational Molecular Biology Distinguished Lecture Series
"Using Motion Planning to Study Molecular Motions"
Nancy Amato, Ph.D., Parasol Lab, Department of Computer Science, Texas A&M University
Wednesday, October 10, 2007 at 4:00 P.M.
Room 241 Swig Boardroom (2nd Floor CIT)
Protein motions, ranging from molecular flexibility to large-scale conformational change, play an essential role in many biochemical processes. For example, some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. Despite the explosion in our knowledge of structural and functional data, our understanding of protein movement is still very limited because it is difficult to measure experimentally and computationally expensive to simulate.
In this talk we describe a method we have developed for modeling protein motions that is based on probabilistic roadmap methods (PRM) for motion planning. Our technique yields an approximate map of a protein's potential energy landscape and can be used to generate transitional motions of a protein to the native state from unstructured conformations or between specified conformations. We describe a method based on rigidity theory that allows us to sample conformation space more efficiently than our initial sampling strategy and enables us to study a broader range of motions for larger proteins and new analysis tools that enable us to extract kinetics information, such as folding rates. For example, we show that rigidity-based sampling results in maps that capture subtle folding differences between protein G and its mutations, NuG1 and NuG2, and we illustrate how our technique can be used to study large-scale conformational changes in calmodulin, a 148 residue signaling protein known to undergo conformational changes when binding. More information regarding our work, including an archive of protein motions generated with our technique, are available from our protein folding server: http://parasol.tamu.edu/foldingserver/
BIO: Nancy M. Amato is a professor of Computer Science at Texas A&M University. She received B.S. and A.B. degrees in Mathematical Sciences and Economics, respectively, from Stanford University, and M.S. and Ph.D. degrees in Computer Science from UC Berkeley and the University of Illinois at Urbana-Champaign, respectively. She was an AT&T Bell Laboratories PhD Scholar, she is a recipient of a CAREER Award from the National Science Foundation. She served as an Associate Editor of the IEEE Transactions on Robotics and Automation, is currently an Associate Editor of the IEEE Transactions on Parallel and Distributed Systems, she serves on review panels for NIH and NSF, and she regularly serves on conference organizing and program committees. She is a member of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W) and she co-directs the CRA-W's Distributed Mentor Program (http://www.cra.org/Activities/craw/dmp/).
Her main areas of research focus are motion planning, computational biology and geometry, and high-performance computing. Current projects include the development of a new technique for approximating protein folding pathways and energy landscapes, and STAPL, a parallel C++ library enabling the development of efficient, portable parallel programs. http://parasol.tamu.edu/~amato
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