Achieving high specificity in biochemical transformations is crucial for research and therapeutics. This is particularly important for genome editing, where enhancing tool specificity ensures effective and precise editing outcomes1,2. Current strategies are constrained by activity-specificity trade-offs, high labour intensity and low success rates3,4. Here we present ContactSeek, an artificial-intelligence-driven framework that uses AlphaFold3 (AF3)-predicted contact probability5 to improve the specificity of genome editors. Using Cas9-TadA adenine base editors6-8 as a demonstration, we mapped their genome-wide off-targets and fed the off-target DNA sequences to AF3. Among AF3 outputs, we found that contact probability was more sensitive than predicted three-dimensional structures for detecting differential interactions between on- and off-target complexes. Correlating contact probability with sequencing-based off-target signals, ContactSeek identified and ranked consensus contact regions, which are neighbouring Cas residues with consistent contact changes to DNA/guide RNA, and pinpointed specificity-determining residues within them. ContactSeek can also be applied modularly and identified key residues in the TadA8e deaminase. Targeted amplicon sequencing, genome-wide profiling, R-loop assay and RNA-sequencing together confirmed the greatly enhanced specificity; our best variant, combining two mutations of Cas9 and TadA8e, outperformed several known high-fidelity adenine base editors. ContactSeek is also generalized to Cas12a-based cytosine base editors. Collectively, our framework represents an AF3-driven model tailored for specificity improvement, establishing a paradigm for improving the precision of genome editing tools through the integration of structural and functional dimensions.
CRISPR Gene Editing