Anzalone, A. V., Koblan, L. W. & Liu, D. R. Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors. Nat. Biotechnol. 38, 824–844 (2020).
Doudna, J. A. The promise and challenge of therapeutic genome editing. Nature 578, 229–236 (2020).
Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009).
Packer, M. S. & Liu, D. R. Methods for the directed evolution of proteins. Nat. Rev. Genet. 16, 379–394 (2015).
Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).
Gaudelli, N. M. et al. Programmable base editing of A*T to G*C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).
Richter, M. F. et al. Phage-assisted evolution of an adenine base editor with improved Cas domain compatibility and activity. Nat. Biotechnol. 38, 883–891 (2020).
Lapinaite, A. et al. DNA capture by a CRISPR-Cas9-guided adenine base editor. Science 369, 566–571 (2020).
Jeong, Y. K. et al. Adenine base editor engineering reduces editing of bystander cytosines. Nat. Biotechnol. 39, 1426–1433 (2021).
Chen, L. et al. Engineering a precise adenine base editor with minimal bystander editing. Nat. Chem. Biol. 19, 101–110 (2023).
Wu, Y., Xiao, Y. L. & Tang, W. High-precision cytosine base editors by evolving nucleic-acid-recognition hotspots in deaminase. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02678-w (2025).
Doman, J. L., Raguram, A., Newby, G. A. & Liu, D. R. Evaluation and minimization of Cas9-independent off-target DNA editing by cytosine base editors. Nat. Biotechnol. 38, 620–628 (2020).
Cho, S. I. et al. Engineering TALE-linked deaminases to facilitate precision adenine base editing in mitochondrial DNA. Cell 187, 95–109 (2024).
Kim, N. et al. Prediction of the sequence-specific cleavage activity of Cas9 variants. Nat. Biotechnol. 38, 1328–1336 (2020).
Shi, H. et al. Rapid two-step target capture ensures efficient CRISPR-Cas9-guided genome editing. Mol. Cell 85, 1730–1742 (2025).
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).
Hsu, C., Nisonoff, H., Fannjiang, C. & Listgarten, J. Learning protein fitness models from evolutionary and assay-labeled data. Nat. Biotechnol. 40, 1114–1122 (2022).
Meier, J. et al. Language models enable zero-shot prediction of the effects of mutations on protein function. Adv. Neural Inform. Process. Syst. 34, 29287–29303 (2021).
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Shanker, V. R., Bruun, T. U. J., Hie, B. L. & Kim, P. S. Unsupervised evolution of protein and antibody complexes with a structure-informed language model. Science 385, 46–53 (2024).
Fei, H. et al. Advancing protein evolution with inverse folding models integrating structural and evolutionary constraints. Cell 188, 4674–4692 (2025).
Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).
Bravo, J. P. K. et al. Structural basis for mismatch surveillance by CRISPR-Cas9. Nature 603, 343–347 (2022).
Pacesa, M. et al. Structural basis for Cas9 off-target activity. Cell 185, 4067–4081 (2022).
Lei, Z., Meng, H., Zhuang, Y., Zhu, Q. & Yi, C. Chemical and biological approaches to interrogate off-target effects of genome editing tools. ACS Chem. Biol. 18, 205–217 (2023).
Tao, J., Bauer, D. E. & Chiarle, R. Assessing and advancing the safety of CRISPR-Cas tools: from DNA to RNA editing. Nat. Commun. 14, 212 (2023).
Lopes, R. & Prasad, M. K. Beyond the promise: evaluating and mitigating off-target effects in CRISPR gene editing for safer therapeutics. Front. Bioeng. Biotechnol. 11, 1339189 (2024).
Lei, Z. et al. Detect-seq reveals out-of-protospacer editing and target-strand editing by cytosine base editors. Nat. Methods 18, 643–651 (2021).
Lei, Z. et al. Mitochondrial base editor induces substantial nuclear off-target mutations. Nature 606, 804–811 (2022).
Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).
Liao, J. et al. Therapeutic adenine base editing of human hematopoietic stem cells. Nat. Commun. 14, 207 (2023).
Mayuranathan, T. et al. Potent and uniform fetal hemoglobin induction via base editing. Nat. Genet. 55, 1210–1220 (2023).
United States Patent and Trademark Office, US Patent No. 11,752,202 B2 Compositions and Methods for Treating Hemoglobinopathies. Beam Therapeutics Inc.
Tian, R. et al. Massively parallel CRISPR off-target detection enables rapid off-target prediction model building. Med 4, 478–492 (2023).
Tsai, S. Q. et al. CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14, 607–614 (2017).
Yuan, K. et al. Selict-seq profiles genome-wide off-target effects in adenosine base editing. Nucleic Acids Res. 53, gkaf281 (2025).
Zhu, M. et al. Tracking-seq reveals the heterogeneity of off-target effects in CRISPR-Cas9-mediated genome editing. Nat. Biotechnol. 43, 799–810 (2025).
Losey, H. C., Ruthenburg, A. J. & Verdine, G. L. Crystal structure of Staphylococcus aureus tRNA adenosine deaminase TadA in complex with RNA. Nat. Struct. Mol. Biol. 13, 153–159 (2006).
Pacesa, M. et al. R-loop formation and conformational activation mechanisms of Cas9. Nature 609, 191–196 (2022).
Chen, J. S. et al. Enhanced proofreading governs CRISPR-Cas9 targeting accuracy. Nature 550, 407–410 (2017).
Grunewald, J. et al. CRISPR DNA base editors with reduced RNA off-target and self-editing activities. Nat. Biotechnol. 37, 1041–1048 (2019).
Zhou, C. et al. Off-target RNA mutation induced by DNA base editing and its elimination by mutagenesis. Nature 571, 275–278 (2019).
Rees, H. A., Wilson, C., Doman, J. L. & Liu, D. R. Analysis and minimization of cellular RNA editing by DNA adenine base editors. Sci. Adv. 5, eaax5717 (2019).
Perrotta, R. M. et al. Engineered base editors with reduced bystander editing through directed evolution. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02937-w (2025).
Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).
Yamano, T. et al. Crystal structure of Cpf1 in complex with guide RNA and target DNA. Cell 165, 949–962 (2016).
Yang, L. et al. Engineering APOBEC3A deaminase for highly accurate and efficient base editing. Nat. Chem. Biol. 20, 1176–1187 (2024).
Wang, X. et al. Cas12a base editors induce efficient and specific editing with low DNA damage Response. Cell Rep. 31, 107723 (2020).
Wang, J. Y., Pausch, P. & Doudna, J. A. Structural biology of CRISPR-Cas immunity and genome editing enzymes. Nat. Rev. Microbiol. 20, 641–656 (2022).
Kryshtafovych, A. et al. Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15. Proteins 91, 1903–1911 (2023).
Lu, D., Yu, S., Huang, Y. & Gong, X. Multimeric protein interaction and complex prediction: Structure, dynamics and function. Comput. Struct. Biotechnol. J. 27, 1975–1997 (2025).
Masters, M. R., Mahmoud, A. H. & Lill, M. A. Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions. Nat. Commun. 16, 8854 (2025).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Krishna, R. et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom. Science 384, eadl2528 (2024).
Zou, S., Sun, Y. & Tang, W. Charting the development and engineering of CRISPR base editors: lessons and inspirations. Cell Chem. Biol. 32, 789–808 (2025).
Song, J., Zhuang, Y. & Yi, C. Programmable RNA base editing via targeted modifications. Nat. Chem. Biol. 20, 277–290 (2024).
Lei, Z., Meng, H., Rao, X., Zhao, H. & Yi, C. Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors. Nat. Protoc. 18, 2221–2255 (2023).
Zhuang, Y. et al. Increasing the efficiency and precision of prime editing with guide RNA pairs. Nat. Chem. Biol. 18, 29–37 (2022).
Arbab, M. et al. Determinants of base editing outcomes from target library analysis and machine learning. Cell 182, 463–480 (2020).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Meng, H., Zhang, S. & Yi, C. Customized code for dI-profiling analysis (v.1.0.0). Zenodo https://doi.org/10.5281/zenodo.20487721 (2026).
Zhang, Y., Park, C., Bennett, C., Thornton, M. & Kim, D. Rapid and accurate alignment of nucleotide conversion sequencing reads with HISAT-3N. Genome Res. 31, 1290–1295 (2021).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Eastman, P. et al. OpenMM 8: molecular dynamics simulation with machine learning potentials. J. Phys. Chem. B 128, 109–116 (2024).
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).
Zgarbova, M. et al. Refinement of the sugar-phosphate backbone torsion beta for AMBER force fields improves the description of Z- and B-DNA. J. Chem. Theory Comput. 11, 5723–5736 (2015).
Zgarbova, M. et al. Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput. 7, 2886–2902 (2011).
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: an N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).
Rallapalli, K. L. & Komor, A. C. The design and application of DNA-editing enzymes as base editors. Annu. Rev. Biochem. 92, 43–79 (2023).
Xiao, Y. L., Wu, Y. & Tang, W. An adenine base editor variant expands context compatibility. Nat. Biotechnol. 42, 1442–1453 (2024).
Ranzau, B. L., Rallapalli, K. L., Evanoff, M., Paesani, F. & Komor, A. C. The wild-type tRNA adenosine deaminase enzyme TadA is capable of sequence-specific DNA base editing. ChemBioChem 24, e202200788 (2023).
Shi, K. et al. Structural basis for targeted DNA cytosine deamination and mutagenesis by APOBEC3A and APOBEC3B. Nat. Struct. Mol. Biol. 24, 131–139 (2017).
Glasscock, C. J. et al. Computational design of sequence-specific DNA-binding proteins. Nat. Struct. Mol. Biol. 32, 2252–2261 (2025).
Sugiki, S., Niide, T., Toya, Y. & Shimizu, H. Logistic regression-guided identification of cofactor specificity-contributing residues in enzyme with sequence datasets partitioned by catalytic properties. ACS Synth. Biol. 11, 3973–3985 (2022).
Meng, H. et al. Dataset: AF3 prediction for DNA-RNA-Cas protein ternary complex. Zenodo https://doi.org/10.5281/zenodo.17197986 (2026).
Meng, H. et al. Dataset: AF3 prediction for protein-NA and protein-ligand complex. Zenodo https://doi.org/10.5281/zenodo.19555204 (2026).
Meng, H. et al. Dataset: targeted amplicon sequencing (Target-seq) dataset for on- and off-target site analysis. Zenodo https://doi.org/10.5281/zenodo.17243125 (2026).
Meng, H., Zhang, S. & Yi, C. ContactSeek code v1.0.0. https://doi.org/10.5281/zenodo.20487746 (2026).
