Enhancing base editing precision via directed evolution

Base editors enable precise genome modification but are constrained by bystander edits that limit their applicability. Existing strategies to enhance precision often compromise efficiency and remain highly sequence dependent. Here, we present a parallel engineering approach that optimizes both guide RNAs and the deaminase enzyme to minimize bystander editing without sacrificing activity. We designed a library of 3-extended guide RNAs (agRNAs) and identified context-dependent variants that improved specificity. Using a precision-driven phage-assisted evolution platform and protein language models, we evolved adenine base editor variants that drastically reduce bystander edits while maintaining high efficiency. Our findings establish a scalable framework for precision engineering of base editors, overcoming a fundamental barrier in genome editing.