TPP Riboswitch Design Using Secondary-Structure-Informed Geometric GNN

RNA inverse design is an essential part of many synthetic biology applications. The current machine learning approaches can effectively predict sequence of the RNA from its 3D atomic-scale information and even generate hypothetical sequences predicted to have similar structural behavior to a desired template. Natural riboswitches are RNA molecules capable of switching their conformation upon sensing an environmental signal and are great candidates for synthetic RNA devices. Design of a novel riboswitch, however, is a formidable challenge, due to inherent plasticity of certain RNA structures and also given that there is little structural similarity among the already discovered riboswitch classes making it difficult to combine structural information. In this work, we explore the capabilities of the GNN-based structure-informed RNA inverse design tool, to inverse design the Thiamine Pyrophosphate (TPP) riboswitch from its backbone geometry. Results show that sequence recovery of TPP riboswitches is higher for models containing solo RNA structures compared to those containing other types of riboswitches as well as those containing RNA-Protein complexes. Design of a bound state required further modeling of ligand binding as well as impact of metal ions on structure.