ArticleNature communications2026
Engineering the MmeFz2-ωRNA system for efficient genome editing through an integrated computational-experimental framework.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors.Nature biotechnology · 2026Article
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Authors and funding
16 authors.
Funding
Abstract
Eukaryotic Fanzor proteins are compact, programmable RNA-guided nucleases with substantial potential for genome editing, although their efficiency in mammalian cells remains suboptimal. Here, we present a combinatorial engineering strategy to optimize a representative Fanzor system, MmeFz2-ωRNA. AlphaFold3-powered rational redesign produced a minimized ωRNA scaffold that is 30% smaller while maintaining up to 82.2% efficiency. Synergistic structure-guided and AI-augmented protein engineering generated two variants, enMmeFz2 and evoMmeFz2, which exhibited an average ~32-fold increase in activity across 38 genomic loci. Moreover, fusion of the non-specific DNA-binding domain HMG-D further enhanced editing performance (enMmeFz2-HMG-D and evoMmeFz2-HMG-D). Notably, evoMmeFz2-HMG-D demonstrated robust in vivo genome editing activity, enabling dystrophin restoration in humanized male Duchenne muscular dystrophy mouse models via single adeno-associated virus (AAV) delivery. This study establishes Fanzor2 as a gene editing platform for genome engineering and therapeutic applications, and underscores the power of AI-guided engineering to accelerate genome editor development while reducing experimental burden.
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Registered trials
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