In recent years, no event has been more consequential than the emergence of the Covid-19 pandemic. Covid-19 has disrupted the globe at an unprecedented scale and has caused profound changes to our daily lives. Our inability to control the pandemic for a year and a half, and counting, has forced a critical reexamination of all aspects of our social system. Other factors aside, one would conclude that our knowledge of viruses is rudimentary and our understanding of their chemistry is narrow. Yet, Covid-19 is essentially just a single-stranded RNA. RNA and its more famous cousin DNA have been known for a very long time. The DNA duplex structure, which is ingrained in the popular knowledge of chemistry and biology, was discovered in the 1950s, and DNA was first identified in the late 1860s. This history notwithstanding, the Covid-19 pandemic does indeed prove that our knowledge of DNA and RNA, their chemistry and their biology, is limited. It is a fascinating feature of science that the familiar and the mysterious are intertwined. Some under-explored facets of DNA and RNA, though none are of immediate relevance to Covid-19, are the topic of this thesis. The first question that the thesis asks is how the chemical structure of nucleic acids dictates their three-dimensional geometry. A survey of the class of nucleic acid polymers, of which DNA and RNA are just two manifestations, reveals a remarkable diversity in their structural organizations. In fact, DNA itself can adopt varying geometries, from the familiar B-form helix to the less common, RNA-like A-form conformation. To answer this question, Chapter 2 develops a general framework and a software program, the proto-Nucleic Acid Builder, for the prediction of the three-dimensional structure of nucleic acid polymers. The program models the structure of DNA and RNA and their analogs (XNAs) as being dependent on the helical organization of the nucleobases and the specific arrangement of the backbone atoms analyzed in terms of the backbone torsional angles. The three-dimensional structures where the nucleobase orientation and the backbone torsional angles are compatible and where the atoms have favorable noncovalent interactions are the physically possible structures for nucleic acid polymers. Chapter 2 develops a methodology for predicting the structure of DNA, RNA, and their analogs in isolation. However, interactions between nucleic acids and their surrounding can significantly impact their structure and function. Chapter 3 explores this area by modeling the interaction between DNA/RNA and a small organic molecule, cyanuric acid, in solution. Molecular dynamics simulations reveal a novel noncovalent helicene structure where three poly(adenosine) oligomers and cyanuric acid molecules form a continuous helical hydrogen-bond network. The balance between stacking interactions, hydrogen bonding interactions, and backbone preorganization determines the structure of these remarkable supramolecular assemblies. Stacking and hydrogen-bonding interactions between nucleobases are largely responsible for the stability of DNA and RNA in solution. Nevertheless, when those nucleobases are mixed in the absence of a backbone, they fail to self-assemble in solution, raising the question of how these nucleobases were originally selected for information transfer. One proposed solution is that alternative nucleobases capable of self-assembly, such as cyanuric acid and triaminopyrimidine, were the original information carriers. In Chapter 4, we explore the structure and noncovalent interactions of one such supramolecular polymer using experimental and computational tools. We confirm the ability of these bases to form extended hexameric rosette structures. Then, we analyze the properties of these assemblies, including their highly sensitive helical structure and unusual stiffness, and compare them to the properties of DNA and RNA. We show that the properties of these supramolecular assemblies stem from the underlying noncovalent interactions between the bases. In Chapter 5, we explore how we can model noncovalent interactions both accurately and realistically for macromolecules, such as proteins and DNA, an essential question if we want practically meaningful theoretical models. Accurate quantum mechanical methods are computationally intensive and therefore are typically limited to a few hundred atoms. By contrast, classical methods are applicable to large systems, albeit with a reduced accuracy. We show that these two approaches can be combined for accurate and affordable modeling of noncovalent interactions. Specifically, we couple symmetry adapted perturbation theory, a quantum mechanical method, with an external classical potential represented by point charges, and we show that interaction energies and their decomposition to electrostatics, exchange-repulsion, induction/polarization, and London dispersion components can be accurately computed.
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