Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors

Abstract

Efficient protein engineering is constrained by vast sequence space and limited experimental throughput, particularly for protein families that lack large mutational datasets. Here we combine Fanzor2 (Fz2) ortholog discovery, ωRNA scaffold engineering and EvoMax, a model-guided prioritization strategy for sparse-data engineering of compact eukaryotic Fz2 nucleases. EvoMax integrates iterative experimental profiling with Gaussian process regression, protein language models and inverse folding to navigate complex sequence-to-fitness landscapes. Applied to eukaryotic Fz2 nucleases, this strategy yielded a high-performance variant, FanzMAX v3-hLa, achieving up to 97% editing efficiency at the best-performing endogenous locus and a mean editing efficiency of ~33% across 19 endogenous loci, outperforming the established compact genome editors enNlovFz2 and enCnCas12f1 by more than 2.6-fold. In vivo editing of hPCSK9 in humanized mice supported the translational potential of optimized Fz2 editors. Together, these results establish EvoMax as an integrated strategy for engineering compact eukaryotic Fz2 genome editors and identify FanzMAX v3-hLa as a high-efficiency programmable nuclease for mammalian genome editing. Computational protein evolution with sparse data generates efficient small RNA-guided nucleases.