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Abstract
Despite many years of experimental effort, the factors that impact stacking interactions involving DNA have proved difficult to unravel. This incomplete understanding of these fundamental interactions hinders efforts to design molecules that harness these interactions for sensing and therapeutic purposes. Modern computational tools are well-suited to model DNA-containing systems, and this dissertation describes the application of a variety of these tools to understand stacking interaction involving nucleobase through studies of nucleobase targeting sensors, DNA intercalators, and the stacking of natural and artificial nucleobases. First, I analyze the stacking and hydrogen bonding interactions operating in a recently developed sensor for guanine. The results show that the binding strength can be modulated by a number of factors, including both directly through changing the size and shape of a pendant aryl group that engages in stacking interactions with the bound guanine and indirectly via changes in the number and quality of hydrogen-bonding interactions between the bound guanine and the linker connecting the pendant aryl group and the rest of the sensor. Second, I establish guidelines for the quantum mechanical (QM) study of DNA intercalators, demonstrated the performance of a range of density functional theory (DFT) methods and their limitations when applied to intercalated DNA. Furthermore, I show that the required size of the DNA binding site model in such computations depends on the binding mode of the intercalator and its protonation state. Finally, I develop a quantitative and qualitative model for the intrinsic stacking of natural and artificial nucleobases. Using this predictive model, stacking interaction energies between artificial and natural nucleobases can be rapidly predicted based solely on a descriptor derived from the electrostatic potentials (ESPs) of the nucleobases as well as their heavy-atom counts. This ESP-derived descriptor plainly illustrates how the unique electrostatic character of the different nucleobases modulates the strength of stacking interactions. Together, these studies provide key new insights into the nature of stacking interactions, with important implications for understanding their role in biological systems and exploiting these interactions in numerous biological and non-biological contexts.