AI-Designed Synthetic CRISPR Enzymes: Evolving Molecular Scissors Beyond Natural Constraints: AI-Designed Synthetic CRISPR Enzymes

Shaista Khalid

doi:10.54393/fbt.v6i2.245

Authors

Shaista Khalid Department of Biotechnology, Forman Christian College, University, Lahore, Pakistan

DOI:

https://doi.org/10.54393/fbt.v6i2.245

Keywords:

CRISPR-Cas9, Generative AI, Protein Language Models, Protein Design, PAM Engineering, Gene Editing, Synthetic Biology, Biosecurity

Abstract

Therapeutic gene editing has historically relied on naturally evolved bacterial nucleases (SpCas9, Cas12a), imposing constraints: rigid protospacer-adjacent motif (PAM) requirements, protein sizes challenging adeno-associated virus packaging, and pre-existing human immunity. This narrative review assesses the paradigm shift from natural-product discovery toward generative artificial intelligence (AI) for engineering non-natural CRISPR-Cas nucleases with user-defined properties. This study synthesizes the design–build–test–learn pipeline encompassing protein language models (ESM3), diffusion-based structure generators (RFdiffusion, ProteinMPNN), and property-specific architectures (Protein2PAM). High-throughput platforms like Sequence Display generate >10 million sequence–activity data points per experiment for closed-loop validation. AI-generated OpenCRISPR-1 (>400 mutations from SpCas9) matches natural on-target efficiency while achieving sub-1% median off-target indels and up to 95% reduction at specific sites. Protein2PAM-evolved Nme1Cas9 variants show broadened PAM tolerance and up to 50-fold increased activity without iterative evolution. Generative AI addresses PAM restriction, packaging, and immunogenicity simultaneously. However, in vivo validation, long-term genotoxicity data, and biosecurity governance gaps remain unresolved.

References

Ganguly C, Rostami S, Long K, Aribam SD, Rajan R. Unity among the Diverse RNA-guided CRISPR-Cas Interference Mechanisms. Journal of Biological Chemistry. 2024 Jun; 300(6). doi: 10.1016/j.jbc.2024.107295.

Zetsche B, Gootenberg JS, Abudayyeh OO, Slaymaker IM, Makarova KS, Essletzbichler P et al. Cpf1 is A Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas System. Cell. 2015 Oct; 163(3): 759-71. doi: 10.1016/j.cell.2015.09.038.

Alexander LM, Aliaga Goltsman DS, Liu J, Lin JL, Temoche-Diaz MM, Laperriere SM et al. Novel and Engineered Type II CRISPR Systems from Uncultivated Microbes with Broad Genome Editing Capability. The CRISPR Journal. 2023 Jun; 6(3): 261-77. doi: 10.1089/crispr.2022.0090.

Al-Shayeb B, Skopintsev P, Soczek KM, Stahl EC, Li Z, Groover E et al. Diverse Virus-Encoded CRISPR-Cas Systems Include Streamlined Genome Editors. Cell. 2022 Nov; 185(24): 4574-86. doi: 10.1016/j.cell.2022.10.020.

Sun A, Li CP, Chen Z, Zhang S, Li DY, Yang Y et al. The compact Casπ (Cas12l) Bracelet Provides a Unique Structural Platform for DNA Manipulation. Cell Research. 2023 Mar; 33(3): 229-44. doi: 10.1038/s41422-022-00771-2.

Matsoukas IG. Prime Editing: Genome Editing for Rare Genetic Diseases Without Double-Strand Breaks or Donor DNA. Frontiers in Genetics. 2020 Jun; 11: 527211. doi: 10.3389/fgene.2020.00528.

Serpa G, Gong Q, De M, Rana PS, Montgomery CP, Wozniak DJ et al. Detection of Pre-Existing Immunity to Bacterial Cas9 Proteins in People with Cystic Fibrosis. Immunohorizons. 2025 Oct; 9(10): vlaf041. 10.1093/immhor/vlaf041.

Mehta A and Merkel OM. Immunogenicity of Cas9 Protein. Journal of Pharmaceutical Sciences. 2020 Jan; 109(1): 62-7. doi: 10.1016/j.xphs.2019.10.003.

Collias D and Beisel CL. CRISPR Technologies and the Search for the PAM-Free Nuclease. Nature Communications. 2021 Jan; 12(1): 555. doi: 10.1038/s41467-020-20633-y.

Ruffolo JA, Nayfach S, Gallagher J, Bhatnagar A, Beazer J, Hussain R et al. Design of Highly Functional Genome Editors by Modelling CRISPR–Cas Sequences. Nature. 2025 Sep; 645(8080): 518-25. doi: 10.1038/s41586-025-09298-z.

Nayfach S, Bhatnagar A, Novichkov A, Kim N, Hoffnagle AM, Hussain R et al. Customizing CRISPR–Cas PAM Specificity with Protein Language Models. Nature Biotechnology. 2026 Feb: 1-0. doi: 10.1038/s41587-025-02995-0.

Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W et al. Evolutionary-Scale Prediction of Atomic-Level Protein Structure with a Language Model. Science. 2023 Mar; 379(6637): 1123-30. doi: 10.1126/science.ade2574.

Hayes T, Rao R, Akin H, Sofroniew NJ, Oktay D, Lin Z et al. Simulating 500 Million Years of Evolution with A Language Model. Science. 2025 Feb; 387(6736): 850-8. doi: 10.1126/science.ads0018.

Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE et al. De Novo Design of Protein Structure and Function with RFdiffusion. Nature. 2023 Aug; 620(7976): 1089-100. doi: 10.1038/s41586-023-06415-8.

Dauparas J, Anishchenko I, Bennett N, Bai H, Ragotte RJ, Milles LF et al. Robust Deep Learning–Based Protein Sequence Design using ProteinMPNN. Science. 2022 Oct; 378(6615): 49-56. doi: 10.1126/science.add2187.

Qu Y, Huang K, Yin M, Zhan K, Liu D, Yin D et al. CRISPR-GPT for Agentic Automation of Gene-Editing Experiments. Nature Biomedical Engineering. 2026 Feb; 10(2): 245-58. doi: 10.1038/s41551-025-01463-z.

Cheng L, Zheng X, Jiang SJ, Hu Y, Liu Y, Yang K et al. Sequence Display Enables Large-Scale Sequence–Activity Datasets for Rapid Protein Evolution. Nature Biotechnology. 2026 Apr: 1-5. doi: 10.1038/s41587-026-03087-3.

Frangoul H, Altshuler D, Cappellini MD, Chen YS, Domm J, Eustace BK et al. CRISPR-Cas9 Gene Editing for Sickle Cell Disease and Β-Thalassemia. New England Journal of Medicine. 2021 Jan; 384(3): 252-60. doi: 10.1056/NEJMoa2031054.

Wittmann BJ, Alexanian T, Bartling C, Beal J, Clore A, Diggans J et al. Strengthening Nucleic Acid Biosecurity Screening Against Generative Protein Design Tools. Science. 2025 Oct; 390(6768): 82-7. doi: 10.1126/science.adu8578.

Wheeler NE. Responsible AI in Biotechnology: Balancing Discovery, Innovation and Biosecurity Risks. Frontiers in Bioengineering and Biotechnology. 2025 Feb; 13: 1537471. doi: 10.3389/fbioe.2025.1537471.

Ruffolo JA, Chu LS, Mahajan SP, Gray JJ. Fast, Accurate Antibody Structure Prediction from Deep Learning on Massive Set of Natural Antibodies. Nature Communications. 2023 Apr; 14(1): 2389. doi: 10.1038/s41467-023-38063-x.

Nidhi S, Anand U, Oleksak P, Tripathi P, Lal JA, Thomas G et al. Novel CRISPR–Cas Systems: An Updated Review of the Current Achievements, Applications, and Future Research Perspectives. International Journal of Molecular Sciences. 2021 Mar; 22(7): 3327. doi: 10.3390/ijms22073327.

Koonin EV, Gootenberg JS, Abudayyeh OO. Discovery of Diverse CRISPR-Cas Systems and Expansion of the Genome Engineering Toolbox. Biochemistry. 2023 May; 62(24): 3465. doi: 10.1021/acs.biochem.3c00159.

Chatterjee P, Lee J, Nip L, Koseki SR, Tysinger E, Sontheimer EJ et al. A Cas9 with PAM Recognition for Adenine Dinucleotides. Nature Communications. 2020 May; 11(1): 2474. doi: 10.1038/s41467-020-16117-8.

Alkhnbashi OS, Meier T, Mitrofanov A, Backofen R, Voss B. CRISPR-Cas Bioinformatics. Methods. 2020 Feb; 172: 3-11. doi: 10.1016/j.ymeth.2019.07.013.

Edison R, Toner S, Esvelt KM. Evaluating the Robustness of Current Nucleic Acid Synthesis Screening. Preprint. 2024.

Ferdosi SR, Ewaisha R, Moghadam F, Krishna S, Park JG, Ebrahimkhani MR et al. Multifunctional CRISPR-Cas9 with Engineered Immunosilenced Human T Cell Epitopes. Nature Communications. 2019 Apr; 10(1): 1842. doi: 10.1038/s41467-019-09693-x.

Skopintsev P, Esain-Garcia I, DeTurk EC, Yoon PH, Zhou Z, Weiss T et al. Structure and Evolution-Guided Design of Minimal RNA-Guided Nucleases. Science. 2026 Jul; 393(6808): 313-8. doi: 10.1126/science.aed6123.

Silverstein RA, Kim N, Kroell AS, Walton RT, Delano J, Butcher RM et al. Custom CRISPR–Cas9 PAM Variants Via Scalable Engineering and Machine Learning. Nature. 2025 Jul; 643(8071): 539-50. doi: 10.1038/s41586-025-09021-y.

Walton RT, Christie KA, Whittaker MN, Kleinstiver BP. Unconstrained Genome Targeting with Near-PAMless Engineered CRISPR-Cas9 Variants. Science. 2020 Apr; 368(6488): 290-6. doi: 10.1126/science.aba8853.

Simhadri VL, McGill J, McMahon S, Wang J, Jiang H, Sauna ZE. Prevalence of Pre-Existing Antibodies to CRISPR-Associated Nuclease Cas9 in the USA Population. Molecular Therapy Methods and Clinical Development. 2018 Sep; 10: 105-12. doi: 10.1016/j.omtm.2018.06.006.

Wang D, Tan Z, Gao J, Zhang S, Shen J, Lu Y. AI4Protein: Transforming the Future of Protein Design. Science China Life Sciences. 2025 Oct; 68(10): 2880-90. doi: 10.1007/s11427-024-2906-3.

Fang T, Bogensperger L, Feer L, Allam A, Bezshapkin V, Balázs Z et al. Uncovering Cas9 PAM Diversity Through Metagenomic Mining and Machine Learning. Nature Communications. 2026 Feb; 17(1): 2510. doi: 10.1038/s41467-026-69098-5.

Karimian A, Azizian K, Parsian H, Rafieian S, Shafiei‐Irannejad V, Kheyrollah M et al. CRISPR/Cas9 Technology as a Potent Molecular Tool for Gene Therapy. Journal of Cellular Physiology. 2019 Aug; 234(8): 12267-77. doi: 10.1002/jcp.27972.

She L, Liang Z, Zou Q, Huo YX. Engineering DNA-Targeting CRISPR and CRISPR-Like Effectors: Advances from Rational Design and High-Throughput Screening to AI-Driven Development. ACS Synthetic Biology. 2026. doi: 10.1021/acssynbio.6c00386.