New protein engineering strategy optimizes compact genome editors with limited data
Researchers developed EvoMax, a model-guided prioritization strategy that optimizes compact eukaryotic Fanzor2 nucleases using sparse experimental data. The method combines iterative experimental profiling with Gaussian process regression, protein language models, and inverse folding to navigate sequence-to-fitness landscapes. The team also performed Fanzor2 ortholog discovery and ωRNA scaffold engineering. The resulting variant, FanzMAX v3-hLa, achieved 97 percent editing efficiency at the best-performing endogenous locus and a mean editing efficiency of around 33 percent across 19 endogenous loci. This outperformed 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 the optimized Fanzor2 editors.
EvoMax addresses a core challenge in protein engineering: vast sequence space and limited experimental throughput, especially for protein families lacking large mutational datasets. The strategy enabled high-efficiency genome editing at multiple endogenous loci and in humanized mice, establishing FanzMAX v3-hLa as a high-efficiency programmable nuclease for mammalian genome editing.
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