Learning the structural diversity in random protein sequence space
The universe of possible protein sequences is astronomically large, yet our understanding of the sequence-structure relationship is confined to the infinitesimal fraction used currently by life. Determining whether "foldable" architectures are rare singularities or accessible solutions is critical for understanding protein evolution and designing novel proteins. Here, we map the structural landscape of random sequence space by screening one million synthetic proteins using a high-throughput in vivo FRET biosensor. We reveal that this space is structurally heterogeneous, populated not only by disordered chains and stress-inducing aggregates but also by "benign" compact structures that resemble globular proteins and evade cellular chaperone responses. By training machine learning models on these phenotypes, we show that structural potential is learnable and generalizes to natural proteomes. These findings demonstrate that biology-like folds are accessible from random sequences with surprising frequency, providing data required to expand generative protein design beyond evolutionary priors.