Coarse-Graining of Molecular Liquids in Time and Space

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Marina Guenza of the University of Oregon is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry. Dr. Guenza develops computational and theoretical methods to study complex molecular liquid. Such computer simulations are useful because they can explain experimental findings based on the molecular nature of the liquid. However, they have severe limitations in the size of the system that they can study. To allow simulation of larger systems, Guenza and coworkers employ a kind of coarse graining, the Integral Equation theory of Coarse-Graining (IECG). This approach uses only essential details in the description of the molecules thereby reducing computational time. This allows for the study of large and more complex systems. The method has been shown to be very accurate in its predictions of structural, thermodynamic, and dynamical properties. Further optimization of the method, its extension to many systems, and making the method accessible to the scientists that want to use it, are the main goals of this project. A second aspect of the project focuses on the further development of an approach to study proteins, nuclear acids and their complexes. Guenza and her research group engage in outreach activities to high school students from rural Oregon school districts. The focus of this research is to extend the range of length scales and time scales where complex molecular liquids can be simulated. In the first project, Guenza and coworkers are extending the capabilities of the Integral Equation theory of Coarse-Graining (IECG) approach to treat non-uniform molecular liquids, for example, at solid interfaces. In the second project, they are working to extend the Langevin Equation for Protein Dynamics (LE4PD) to treat proteins, nucleic acids, and their complexes. The approach is based on a Langevin equation for the dynamics of macromolecules in solution, which also accounts for the presence of local conformational barriers and the internal hydrophobic region in the macromolecule. Developing the theory to describe the long-time barrier-crossing dynamics by integration with Markov State Models is a key component of this work.