Computational investigations into interactions between metal ions and nucleic acids

Abstract

Metal ion contamination is an ongoing global crisis that negatively affects human and environmental health. As a result, metal ion detection and remediation techniques are required. Nucleic acids are promising tools as biosensors for metal detection. Although nucleic acid sensors have been designed for some metal ions (e.g., Cs+, Pb2+), there are metal ion contaminants that require sensing and remediation for which no sensors currently exist. Specifically, with increasing mining and usage of lithium in lithium-ion batteries, rising levels of lithium ions are present in the environment. To design nucleic acid sensing and remediation platforms, it is important to understand how metal ions interact with nucleic acids. Computer modeling can provide valuable insights into structural and binding information of metal–nucleic-acid interactions, thereby aiding the rational design of nucleic-acid-based sensing and remediation solutions for metal ions. This thesis uses different computational techniques, including ab initio (CCSD(T)/CBS), density functional theory (DFT), molecular dynamics (MD), and ab initio molecular dynamics (AIMD) to identify reliable methods to describe metal–nucleic-acid interactions, understand how metal ions interact with nucleic acids, and explore modifications that can capture lithium. The information gained from this thesis lays the groundwork for designing a lithium-specific nucleic acid platform for lithium extraction, which can also support future development of a nucleic acid sensor for lithium. Additionally, insights gained can be applied to designing sensing and extraction platforms for other metal ions, and aid development of therapeutics and materials.