IntroductionSingle-nucleotide substitutions account for over 58% of human disease-causing genetic variations1; correcting these mutations may prevent or even reverse the associated disease progression. Directed by a guide RNA that specifies the target site, CRISPR-associated base editing (BE) can program targeted single-nucleotide conversions without creating double-stranded DNA breaks (DSBs) or requiring a repair template, thus serving as a powerful tool to correct pathogenic point mutations. Current base editors are constructed by fusing an evolved cytosine or adenine deaminase to a catalytically impaired Cas protein (e.g. nSpCas9). To date, five classes of base editors have been developed to mediate versatile single-nucleotide conversions in vitro and in vivo, including cytosine base editors (CBEs) for C·G-to-T·A transitions2,3,4, adenine base editors (ABEs) for A·T-to-G·C transitions5,6, C-to-G base editors (CGBEs) for C·G-to-G·C transversions3,7, and adenine transversion editors (ACBEs) for A·T-to-C·G transversions8.Despite the rapidly growing array of base editors with enhanced editing efficiencies and broader targeting scopes, the accuracy of the base editing technique is compromised by the broad activity windows that span multiple nucleotides within the protospacer. The size of the base editing window is often positively correlated with its activity9. For example, ABE8e—the most efficient ABE variant to date5—exhibits a 10-bp editing window10, much wider than the five-nucleotide activity window of canonical ABEs1. Base editors cannot discriminate the target base when multiple editable nucleotides are present within or near the activity editing window, resulting in bystander single-nucleotide conversions. Notably, approximately 82.3% of human disease-associated mutations that can be corrected by ABEs are located within regions containing multiple adenines (Fig. 1a), suggesting that ABEs may induce undesired mutations when correcting the majority of pathogenic variants. Bystander edit may adversely impact the editing outcome by disrupting the corrected gene function, presenting a significant hurdle to the therapeutic applications of current base editors. Developing efficient base editors with highly focused activity at the intended nucleotide will help address this challenge.Fig. 1: Structure-guided re-engineering of TadA-8e to narrow the base editing window.a Predicted adenine base editing outcomes for correcting known human pathogenic genetic variants. b, General principle for re-engineering TadA-8e. An oligonucleotide-binding module (purple) is introduced into the substrate-binding pocket (yellow), forming stacking interactions (red lines) as well as hydrogen bonds and electrostatic contacts (black lines) with the nucleobases (blue) on the DNA nontarget strand. Cartoons were created in BioRender. jiang, t. (2025) https://BioRender.com/ol36n1g. c Overview of the amino acid substitutions introduced for engineering TadA-NW variants. d An enlarged view of the predicted interactions between the mutated residues (red and pink) with the nontarget DNA strand (green) in the TadA-NW1 substrate binding pocket (yellow) by Pymol (v3.1.3). The original amino acids are in gray. The predicted stacking interaction and other contacts are presented as red and blue dashed lines, respectively. The position of the target base is denoted as “0”, with the two non-target bases at its 5’ end as “−1” and “−2”, respectively. e A-to-G conversion efficiencies of ABE8e and ABE-NW variants at an endogenous locus. The conversion efficiencies were measured by targeted-amplicon high-throughput sequencing (HTS). The protospacer sequence is shown with all the editable adenines highlighted in red, and the PAM sequence is underlined. The most PAM-distal base within the protospacer is counted as protospacer position 1. The heat map represents average editing rates from three independent experiments. f Comparison of bystander (A3 or A9)-to-target (A5) editing ratios for ABE8e and ABE-NW variants within the genomic site shown in (e). Data represent mean ± SEM (n = 3 biologically independent experiments). Statistical significance was calculated using two-tailed Student’s t test comparing each ABE-NW variant to ABE8e; ns = no statistical difference, *P