Al-Hashimi, Hashim MOas, Terrence GOrlovsky, Nicole2022-02-112021https://hdl.handle.net/10161/24352<p>RNA recognition frequently results in conformational changes that optimize</p><p>intermolecular binding. As a consequence, the overall binding affinity of RNA</p><p>to its binding partners depends not only on the intermolecular interactions</p><p>formed in the bound state, but also on the energy cost associated with changing</p><p>the RNA conformational distribution. Measuring these conformational penalties</p><p>is however challenging because bound RNA conformations tend to have equilibrium</p><p>populations in the absence of the binding partner that fall outside detection by</p><p>conventional biophysical methods.</p><p>In this work we employ as a model system HIV-1 TAR RNA and its interaction with</p><p>the ligand argininamide (ARG), a mimic of TAR’s cognate protein binding partner,</p><p>the transactivator Tat. We use NMR chemical shift perturbations (CSP) and NMR</p><p>relaxation dispersion (RD) in combination with Bayesian inference to develop a</p><p>detailed thermodynamic model of coupled conformational change and ligand</p><p>binding. Starting from a comprehensive 12-state model of the equilibrium, we</p><p>estimate the energies of six distinct detectable thermodynamic states that are</p><p>not accessible by currently available methods.</p><p>Our approach identifies a minimum of four RNA intermediates that differ in terms</p><p>of the TAR conformation and ARG-occupancy. The dominant bound TAR conformation</p><p>features two bound ARG ligands and has an equilibrium population in the absence</p><p>of ARG that is below detection limit. Consequently, even though ARG binds to TAR</p><p>with an apparent overall weak affinity ($\Kdapp \approx \SI{0.2}{\milli</p><p>\Molar}$), it binds the prefolded conformation with a $K_{\ch{d}}$ in the nM</p><p>range. Our results show that conformational penalties can be major determinants</p><p>of RNA-ligand binding affinity as well as a source of binding cooperativity,</p><p>with important implications for a predictive understanding of how RNA is</p><p>recognized and for RNA-targeted drug discovery.</p><p>Additionally, we describe in detail the development of our approach for fitting</p><p>complex ligand binding data to mathematical models using Bayesian</p><p>inference. We provide crucial benchmarks and demonstrate the</p><p>robustness of our fitting approach with the goal of application</p><p>to other systems. This thesis aims to provide new insight into</p><p>the dynamics of RNA-ligand recognition as well as provide new</p><p>methods that can be applied to achieve this goal.</p>BiochemistryThermodynamicsStatisticsBayesian inferenceBindingDynamicsNMRRNADissertation