When it comes to deep learning for protein engineering, there is strength in simplicity. In this issue of Cell, with thoughtful deployment of existing fixed-backbone sequence design models, Caixia Gao and colleagues engineer diverse genome editing systems with improved functionality, enabling powerful capabilities in fine-grained and large-scale genome editing as demonstrated through strong experimental validation.