Assessment and Improvement of Computational Models to Study Biological Catalysis

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Assessment and Improvement of Computational Models to Study Biological Catalysis

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A detailed understanding of the molecular mechanisms whereby molecules of RNA can catalyze important reactions such as phosphoryl transfer is fundamental to biology, and of high significance in the development of new biomedical technology. This thesis describes the testing, application and development of quantum models that advance our understanding of the mechanisms of RNA catalysis. Molecular simulations of catalytic mechanisms of RNA require the use of fast, accurate approximate quantum mechanical (QM) models. These models, however, were not necessarily designed and parameterized for biocatalysis. In order to assess the degree to which commonly used approximate QM models are appropriate for biocatalysis applications, a series of models has been tested against a wide range of data sets, including new datasets particularly relevant for RNA catalysis, and compared with high-level benchmark calculations. Results provide new insight into the strengths and weaknesses of these methods, and help to guide next generation model development. We note that both NDDO and SCC-DFTB based QM models fail dramatically in their ability to adequately describe the conformational landscape of DNA and RNA sugar rings. In order to overcome this problem, an empirical sugar pucker energy term has been introduced via multi-dimensional B-spline interpolation of a potential energy surface correction. The corrected semiempirical models closely reproduce the ab initio puckering profiles as well as the barrier of an RNA transesterification model reaction. In addition, a series of RNA transesterification model reactions with various leaving groups have been studied with density-functional calculations in solution to investigate linear free energy relationships (LFERs) and their connection to transition state structure and bonding. These relations can be used to aid in the interpretation of experimental data for non-catalytic and catalytic mechanisms. A driving force in this research has been the development of software infrastructure for scientific computation, including new interfaces to other computational chemistry software, libraries to retrieve information, convert format and apply potentials, and tools for data analysis and visualization.


University of Minnesota Ph.D. dissertation. 2014. Major: Scientific Computation. Advisor: Darrin York. 1 computer file (PDF); 155 pages.

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Huang, Ming. (2014). Assessment and Improvement of Computational Models to Study Biological Catalysis. Retrieved from the University Digital Conservancy,

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