De Novo Design of Polyhedral Protein Assemblies: Before and After the AI Revolution

Abstract Self‐assembling polyhedral protein biomaterials have gained attention as engineering targets owing to their naturally evolved sophisticated functions, ranging from protecting macromolecules from the environment to spatially controlling biochemical reactions. Precise computational design of de novo protein polyhedra is possible through two main types of approaches: methods from first principles, using physical and geometrical rules, and more recent data‐driven methods based on artificial intelligence (AI), including deep learning (DL). Here, we retrospect first principle‐ and AI‐based approaches for designing finite polyhedral protein assemblies, as well as advances in the structure prediction of such assemblies. We further highlight the possible applications of these materials and explore how the presented approaches can be combined to overcome current challenges and to advance the design of functional protein‐based biomaterials.