Structure-guided engineering of AI-derived adenine base editors for nuclear and mitochondrial DNA editing
TL;DR
The results demonstrate that structure-guided refinement of AI-designed deaminases can produce precise, versatile base editors for nuclear and mitochondrial genomes, expanding the research and therapeutic applications of genome editing toolkits.
Abstract
Abstract Traditional adenine base editors (ABEs), primarily derived from TadA, suffer from bystander editing and limited diversity, while artificial intelligence (AI)-designed deaminases such as Deam-P32 exhibit lower efficiency and precision than state-of-the-art editors such as ABE8e. Here, we report the OpenABE variants through the structure-guided evolution of Deam-P32 by optimizing DNA engagement and the base-contacting pocket and incorporating C-terminal extensions from ABE8e. These variants achieved a 16–36-fold increase in A-to-G conversion efficiency over Deam-P32, matching the editing efficiency of ABE8e across 29 endogenous nuclear loci while mitigating bystander cytosine editing. OpenABEs also reduced ATC motif bystander editing and produced fewer guide RNA-independent off-target effects on DNA and RNA. Further, we adapted these variants to mitochondrial DNA editing by designing OpenABE-TALEDs, which yielded editing efficiencies comparable or superior to those of ABE8e-TALEDs. Delivery through engineered virus-like particles further enhanced specificity and product purity. These results demonstrate that structure-guided refinement of AI-designed deaminases can produce precise, versatile base editors for nuclear and mitochondrial genomes, expanding the research and therapeutic applications of genome editing toolkits.