Researchers at Peking University and East China Normal University have unveiled ContactSeek, a novel computational framework that significantly enhances the precision of CRISPR base editing by integrating AlphaFold 3 structural modeling with experimental validation. Led by Chengqi Yi and Dali Li, the team developed the system to address a persistent challenge in genome engineering: minimizing off-target mutations while preserving robust on-target activity. The framework leverages AlphaFold 3 to predict contact probabilities within Cas9-sgRNA-DNA and deaminase-RNA complexes, identifying critical residue interactions that dictate binding specificity. ContactSeek operates by comparing contact probability changes between on-target and mismatch-containing off-target sequences. By mapping these structural shifts, the platform pinpointed consensus contact regions within adenine and cytosine deaminases. Guided by these insights, the researchers engineered next-generation base editors, including ABE8e-DD for adenine editing and Cas12a(R284E)-A3A(H29D) for cytosine editing. Computational predictions were systematically validated using dI-profiling, targeted amplicon sequencing, and transcriptome-wide RNA sequencing. The optimized variants demonstrated up to 99 percent reduction in genome-wide off-target editing events compared to baseline systems, with negligible loss in target-site efficiency. Structural simulations and molecular dynamics further confirmed that targeted mutations destabilize non-canonical binding at mismatched loci without compromising protospacer recognition. The study also extended the methodology to high-fidelity Cas9 variants, such as the K1020D mutation, demonstrating that ContactSeek can retroactively refine existing editors by quantifying contact degradation at off-target sites. The framework’s algorithmic pipeline is agnostic to specific nucleases, offering a scalable strategy for future gene-editing tool development. In alignment with open-science principles, the authors have deposited all AlphaFold 3 prediction outputs, dI-profiling sequencing data, and raw count tables in public repositories, including Zenodo and the National Genomics Data Center. The ContactSeek software suite and analytical code have been publicly released to facilitate adoption across academic and therapeutic biotechnology sectors. This integration of deep learning-driven structural biology with directed evolution marks a substantive advancement toward clinically viable, high-fidelity genome editing.