Navigating protein-nucleic acid sequence-structure landscapes with deep learning

A few years after AlphaFold revolutionised the field of protein structure prediction, the new frontiers and limitations in structural biology have become clearer. Predicting protein-nucleic acid interactions currently stands as one of the major unresolved challenges in the field. This knowledge gap stems from the scarcity and limited diversity of experimental data, as well as the unique geometric, physicochemical, and evolutionary properties of nucleic acids. Despite these challenges, innovative ideas and promising methodological developments have emerged for both predicting protein-nucleic acid complex structures and designing nucleic acids capable of binding to specific protein conformations. This review presents these recent advances and discusses promising avenues, including the integration of high-throughput profiling data, the development of more rigourous and richer evaluation benchmarks, and the discovery of biologically meaningful regulatory and structural signals using self-supervised learning.