Developing a Predictive and Quantitative Understanding of RNA Ligand Recognition

RNA recognition frequently results in conformational changes that optimize

intermolecular binding. As a consequence, the overall binding affinity of RNA

to its binding partners depends not only on the intermolecular interactions

formed in the bound state, but also on the energy cost associated with changing

the RNA conformational distribution. Measuring these conformational penalties

is however challenging because bound RNA conformations tend to have equilibrium

populations in the absence of the binding partner that fall outside detection by

conventional biophysical methods.

In this work we employ as a model system HIV-1 TAR RNA and its interaction with

the ligand argininamide (ARG), a mimic of TAR’s cognate protein binding partner,

the transactivator Tat. We use NMR chemical shift perturbations (CSP) and NMR

relaxation dispersion (RD) in combination with Bayesian inference to develop a

detailed thermodynamic model of coupled conformational change and ligand

binding. Starting from a comprehensive 12-state model of the equilibrium, we

estimate the energies of six distinct detectable thermodynamic states that are

not accessible by currently available methods.

Our approach identifies a minimum of four RNA intermediates that differ in terms

of the TAR conformation and ARG-occupancy. The dominant bound TAR conformation

features two bound ARG ligands and has an equilibrium population in the absence

of ARG that is below detection limit. Consequently, even though ARG binds to TAR

with an apparent overall weak affinity ($\Kdapp \approx \SI{0.2}{\milli

\Molar}$), it binds the prefolded conformation with a $K_{\ch{d}}$ in the nM

range. Our results show that conformational penalties can be major determinants

of RNA-ligand binding affinity as well as a source of binding cooperativity,

with important implications for a predictive understanding of how RNA is

recognized and for RNA-targeted drug discovery.

Additionally, we describe in detail the development of our approach for fitting

complex ligand binding data to mathematical models using Bayesian

inference. We provide crucial benchmarks and demonstrate the

robustness of our fitting approach with the goal of application

to other systems. This thesis aims to provide new insight into

the dynamics of RNA-ligand recognition as well as provide new

methods that can be applied to achieve this goal.