Bridging technical innovation and computational advances in studies of RNA-protein assemblies.

RNA-dependent protein assemblies - including the spliceosome, ribosome and RNA-dependent membraneless organelles - have crucial roles in diverse cellular processes through RNA scaffolding and hierarchical assembly. Various empirical techniques and artificial intelligence algorithms have been developed to help understand the architecture, dynamics and functional implications of RNA-protein complexes, and their further development is underway to comprehensively integrate this information. This Review explores how combining these diverse technologies will enhance our understanding of the biological functions of RNA-dependent protein assemblies. We first explore methodological frontiers, contrasting traditional approaches with new platforms, which enable the identification and tracking of RNA-protein assembly dynamics on the same RNA molecules. We then present avenues for integrating these new experimental techniques with machine-learning methods to improve both predictive models of RNA-protein assembly and functional RNA design. We discuss how the synergy between experimental and digital biology can drive new insights into disease mechanisms and therapeutic strategies, including targeted modulation of pathogenic RNA-protein assemblies. Finally, we examine roadmaps for future research, emphasizing the potential of closed-loop systems that iteratively refine our understanding of RNA-protein assemblies through cycles of hypothesis generation, prediction, experimentation and validation.