Advancing RNA Structural Biology and Therapeutics: Integrating Physical and Data-Driven Computational Approaches

Emerging biomedical advances from precision medicine to synthetic biology highlight RNA's central role as both a regulator and an information carrier. Since RNA function depends on its 3D structure and conformational changes, research in this area focuses on the study of RNA structure along with its folding stability, kinetics, and interactions with metal ions and other molecules. Key questions include: How can the native fold be predicted from the sequence? For a given RNA target, how can RNA-small molecule interactions be predicted and potential drug candidates identified? Developing computational tools to address these questions remains challenging due to limited RNA structural and binding data. To predict 3D structure from sequence, we developed the Vfold pipeline by integrating statistical analysis of RNA structures and molecular dynamics simulations. The Vfold approach showed highly promising results at the international CASP16 competition for biomolecular structure predictions. To design RNA-targeted drugs, we developed the RLDOCK and SPRank pipelines for physics-based and data-driven predictions of ligand-RNA interactions. These models may become useful tools for understanding the structure-based mechanism of RNA function and for accelerating RNA-targeted therapeutic discovery.