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Commun Chem 2025[Dec]; ? (?): ? PMID41345299show ga
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 alpha-, beta-, and alphabeta-solenoid backbones, generating structures that span natural and novel solenoid space. We experimentally characterise 41 solenoid designs, with alpha-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.
Commun+Chem 2025 ; ? (?): ?
