Exploring RNA catalysis and RNA-ligand interactions through molecular simulations

Non-coding RNAs (ncRNAs) have emerged as a central player in gene regulation, catalysis and molecular recognition, reshaping the classical protein-centric view of cellular regulation. The ability to adopt complex three-dimensional architectures enables RNA molecules to act as carriers of information, structural scaffolds, and catalysts. These same structural and chemical features make RNA an increasingly attractive therapeutic target. However, the intrinsic flexibility of RNA, its structural and functional dependence on ions and solvent, and the limited availability of high-resolution structural and affinity data continue to challenge both experimental and computational approaches. Indeed, RNA-targeted drug discovery is confined at an earlier stage compared to its protein-focused counterpart. In this thesis, we address these challenges from two complementary perspectives. First, we employ extensive equilibrium and enhanced sampling molecular dynamics (MD) simulations, to reveal the mechanistic principles that govern RNA catalysis during pre-mRNA splicing. Specifically, we describe how conserved positively charged residues and catalytic metal ions dynamically cooperate within the spliceosome to ensure precise and timely activation of the active site. This mechanistic understanding not only advances our knowledge of RNA-based catalysis but also identifies potential regulatory hotspots that could be exploited for therapeutic modulation. In parallel, we test the largely unexplored computational potentiality and limitations of RNA-ligand relative binding free energy (RBFE) predictions by systematically benchmarking FEP+ on different RNA-small molecules systems. By comparing polarizable and fixed-charge force fields, we demonstrate that Free Energy Perturbation calculations can reliably predict RBFE in RNA systems. Altogether, this work bridges mechanistic insight and methodological innovation, contributing to advance both our understanding of RNA catalysis and our ability to computationally model RNA-ligand interactions for the future development of RNA-targeted therapeutics.