| Abstract |
The distances for certain pairs of atoms in a protein can often be obtained based on our knowledge on various types of bond-lengths and bond-angles or from physical experiments such as nuclear magnetic resonance (NMR). The coordinates of the atoms and hence a protein structure can then be determined by using the known distances. However, it requires the solution of a mathematical problem called the distance geometry problem, which is proved to be computationally intractable in general. In addition, due to insufficient data, such as nuclear overhauser effect (NOE) data in NMR and structural information of comparative models from theoretical methods, the protein structures determined by conventional techniques usually are not as accurate as desired. Therefore, the uses of such protein structures in important applications including homology modeling and rational drug design have been severely limited. In this talk, I will introduce several efficient algorithms including theories for the solution of the distance geometry problem using the idea of geometric build-up. I will also introduce knowledge-based methods for protein structure refinement via constrained optimization and molecular dynamic simulation, in which we construct dedicated structural databases for protein inter-atomic distance distributions and derive so-called mean force potentials to refine NMR-determined protein structures and comparative models.
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| Bio |
EDUCATION
Iowa State University, Ames, Iowa Co-major Ph.D., Bioinformatics and Computational Biology, 2006 Applied Mathematics, 2006
Fudan University, Shanghai, China B.S., Microbiology, 2000
RESEARCH INTERESTS
Bioinformatics and Medical Informatics, Computational Structural Biology, Protein-protein interaction and Rational Drug Design,System Biology, Operations Research, Constrained and Unconstrained Optimization
PROFESSIONAL EXPERIENCES
2006- Assistant Professor, Department of Mathematics, Bioinformatics and Information Sciences Center, Western Kentucky University
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