Abstract

Advancing the performance of programmable genome editing nucleases remains a key challenge in expanding their research and therapeutic applications. Here, we introduce a scalable deep learning–guided protein engineering framework for improving nuclease activity without requiring experimental training data. As a demonstration, we apply this strategy to SpuFz1, a compact Fanzor nuclease of eukaryotic origin, identifying and validating beneficial mutations that produces a multi-mutant variant with an 11.6-fold increase in editing efficiency. In parallel, we use comparative sequence analysis to design and experimentally validate a 75-nt ultrashort ωRNA scaffold, reducing guide RNA length by 79% while maintaining activity. Integration of these optimized components yields enFanzor, a compact genome editing system that achieves editing efficiencies up to 81.9% in mammalian cells, with strong editing performance in both human hematopoietic stem and progenitor cells (HSPCs) and mouse embryos. The outperforming variant developed through this strategy also support robust CBE and ABE activity. Notably, the shortened ωRNA not only improves nuclease editing specificity but also leads to a substantial increase in base editing efficiency. Together, this work demonstrates the power of combining AI-guided protein optimization with rational RNA design, and establishes a generalizable strategy for engineering next-generation genome editing tools.