Deep Learning-Guided Interface Engineering Stabilizes Oligomeric Enzymes

Abstract

The thermostability of oligomeric enzymes is often limited by the intrinsic flexibility of subunit interfaces. Herein, we report a deep learning-driven interface engineering strategy (DeepIE) to systematically stabilize oligomeric enzymes. Applied to a dimeric formate dehydrogenase, we de novo redesigned six flexible regions at the dimer interface. The lead variant retains wild-type catalytic activity while exhibiting an ∼500-fold increase in half-life at 50 °C. Molecular dynamics analyses revealed that reduced local flexibility and strengthened interfacial hydrophobic packing underpin the enhanced thermostability. This work establishes an artificial intelligence-driven, generalizable framework for rational thermostabilization of oligomeric biocatalysts, effectively overcoming the activity–stability trade-off.