Molecular Dynamics-Based Strategies for the Analysis of RNA-Containing Complexes

RNA and RNA–protein complexes are central to many biological processes and represent increasingly relevant targets for pharmaceutical intervention. However, the intrinsic flexibility of RNA, together with the limited availability of experimentally resolved structures, poses significant challenges for computational modeling. Molecular docking approaches are often employed to generate binding hypotheses and rank candidate complexes, although their scoring performance remains limited in this context. Molecular dynamics-based techniques, such as Supervised Molecular Dynamics (SuMD), can be exploited to generate RNA-protein complex structures considering macromolecule flexibility, shifting the primary challenge from sampling to reliable interaction scoring. To address the scoring challenge, this thesis investigates the application of Thermal Titration Molecular Dynamics (TTMD) to RNA-protein systems. TTMD is a technique originally developed to qualitatively estimate the stability of protein-small molecule complexes by measuring the ligand-target interaction persistence during MD simulations, using a fingerprint-based scoring function. In this work, the applicability domain of this recent method was expanded, and its use as a pose refinement tool was assessed for both docking-derived and SuMD-generated nucleic acid complexes. This thesis also presents a new tool, still under development, built on the same interaction fingerprints framework used by TTMD and implemented here to extract MD-sampled positions of interacting atoms, to generate pharmacophore hypotheses that can be prospectively implemented in virtual screening scenarios.