Expanding all-α-helical protein space through rational computational design
De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3–7. As a result, de novo designed proteins are becoming larger and more complex8–10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved structural and functional complexity by modular combination of recurring protein domains13. Approximately 25% of these natural domains are mostly α-helical structures14. Here we show how these can be expanded using rational computational design. Following the domain classification scheme CATH15, we build complex all-α de novo proteins hierarchically using sequence-to-structure relationships for helix-helix interactions, systematic rules to connect helices, computational tools to design loops, and in silico evaluation. The pipeline starts with a target architecture of free-standing helices. These are connected into a topology by considering local arrangements of helical bundles using understood sequence-to-structure relationships for helix packing. Single-chain sequences are completed using template- and AI-based methods. Finally, AlphaFold models are assessed to give small numbers of designs for experimental validation. We test 31 designs for 14 different architectures and 25 topologies. 75% of these express as stable, monomeric, water-soluble proteins; and >30% yield X-ray crystal structures matching the designs to atomic accuracy and with new-to-nature structures. Finally, several of the scaffolds are functionalised through one-shot designs to deliver ion, small-molecule and protein binders.