Designing Novel Solenoid Proteins with In Silico Evolution

Abstract

Solenoid proteins are elongated tandem repeat proteins with diverse biological functions, making them attractive targets for protein design. Advances in machine learning have transformed our understanding of sequence-structure relationships, enabling new approaches for de novo protein design. Here, we present an in silico evolution platform that couples a solenoid discriminator network with AlphaFold2 as an oracle within a genetic algorithm. Starting from random sequences, we design -, {beta}-, and {beta}-solenoid backbones, generating structures that span natural and novel solenoid space. We experimentally characterise 41 solenoid designs, with -solenoids consistently folding as intended, including one structurally validated design that closely matches the design model. All {beta}-solenoids initially failed, reflecting the difficulty of designing {beta}-strand majority proteins. By introducing terminal capping elements and refining designs based on earlier experimental screens, we generate two {beta}-solenoids that have biophysical properties consistent with their designs. Our approach achieves fold-specific hallucination-based design without depending on explicit structural templates.