Learning the structural diversity in random protein sequence space

Abstract

The universe of possible protein sequences is astronomically large, yet our understanding of sequence-structure relationships is derived almost entirely from the infinitesimal fraction used by life. Determining which physical properties are already accessible to unevolved polypeptides, and which require subsequent evolutionary refinement, is central both to understanding protein emergence and to expanding protein design beyond biological sequence space. Here, we explore this largely uncharted space using a high-throughput FRET-based selection of a library containing millions of random protein sequences, complemented by fluorescence imaging, biophysical characterization and sequence-based modelling. We find that random sequence space contains multiple physical phenotypes that cannot be captured by a simple distinction between soluble and aggregated proteins. Individual sequences can produce substantial FRET in both the cytosol and visible assemblies, while assemblies differ in packing density and cellular responses. Repeated selection for stronger FRET reduces population solubility indicating that proximity-based selection alone does not necessarily favor biology-like protein states. These experimentally defined phenotypes are predictable from sequence. Our results reveal resolvable and learnable variation within unevolved protein sequence space, providing a route to investigate how far random sequences lie from soluble, stable protein solutions and to learn from regions of the protein universe not previously sampled by evolution.