Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
Open accessAbstract
Gene editing has the potential to solve fundamental challenges in agriculture, biotechnology, and human health. CRISPR-based gene editors derived from microbes, while powerful, often show significant functional tradeoffs when ported into non-native environments, such as human cells. Artificial intelligence (AI) enabled design provides a powerful alternative with potential to bypass evolutionary constraints and generate editors with optimal properties. Here, using large language models (LLMs) trained on biological diversity at scale, we demonstrate the first successful precision editing of the human genome with a programmable gene editor designed with AI. To achieve this goal, we curated a dataset of over one million CRISPR operons through systematic mining of 26 terabases of assembled genomes and meta-genomes. We demonstrate the capacity of our models by generating 4.8x the number of protein clusters across CRISPR-Cas families found in nature and tailoring single-guide RNA sequences for Cas9-like effector proteins. Several of the generated gene editors show comparable or improved activity and specificity relative to SpCas9, the prototypical gene editing effector, while being 400 mutations away in sequence. Finally, we demonstrate an AI-generated gene editor, denoted as OpenCRISPR-1, exhibits compatibility with base editing. We release OpenCRISPR-1 publicly to facilitate broad, ethical usage across research and commercial applications.
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AI models have already been used to design new CRISPR enzymes, functional proteins, and entire prokaryotic genomes.
“AI models have already been used to design new CRISPR enzymes,53 functional proteins,128 and even entire prokaryotic genomes.51”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [53]
Language-model-created proteins such as esmGFP or OpenCrispr1 are different from any of their naturally occurring counterparts.
“This type of evaluation is already considered within the molecular design space because language-model-created proteins, such as esmGFP52 or OpenCrispr1,53 highlight how different they are from any of their naturally occurring counterparts.”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [53]
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Wang, F., Marouli, A., Charoenwongwatthana, P., & Chang, C. (2025) · Letters in Applied Microbiology · doi:10.1093/lambio/ovaf021 · Cited by 1 · see citing works
Kinetic Mechanism for Fidelity of CRISPR-Cas9 Variants
Hecht, A. D., & Igoshin, O. A. (2025) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5c01154
Blassberg, R. A., Mielczarek, O., Stombaugh, J., Okkenhaug, H., Torres, A. A., Russell, P., Lambourne, J. J., & Porreca, I. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.05.656583
Zanders, E. D. (2025) · Springer eBooks · doi:10.1007/978-3-031-93949-5_5
A Review of AI Tools in Molecular Biology and Virology
Bhat, P. N., Balaji, S., & Shapshak, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-77911-4_30
Harnessing Biotechnology for Climate-Resilient for Crop Innovations
Jassal, P. S., & Marak, J. S. R. (2025) · Springer eBooks · doi:10.1007/978-3-031-84802-5_12
Genome editing techniques for microalgae and yeast
Yıldırım, A., Roy, K., Güler, B. A., İmamoğlu, E., & Amaro, R. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30188-9.00018-4
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Designing proteins with reduced T-cell epitopes through policy optimizationPreprint
Ponnapati, M., Sinha, S., Lynch, B., Boyden, E. S., & Jacobson, J. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.27.678937 · Cited by 1 · see citing works
Advances in artificial intelligence for plant biology and crop breeding: An overview
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Rationally and in silico guided APOBEC3F-directed CBE for enhanced PDAC genetic therapy
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AI-driven tri-typing in agriculture: Current advances, future frontiers
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Agriculture 5.0: Building Resilience by Integrating Social, Technological and Economic Innovations
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Structure-guided discovery and engineering of miniature CRISPR–Cas12m for epigenome editing
Yu, T., Ji, M., Yu, D., Guan, Z., Zhu, R., Jiang, Y., Yang, Z., Qiu, L., Zhang, Z., Mu, J., et al. (2026) · Nature Structural & Molecular Biology · doi:10.1038/s41594-026-01890-9
- Authors
- Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., Meeske, A. J., Cameron, P., & Madani, A.
- Corresponding author
- Madani, Ali
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2024-04-22
- OpenAlex
- W4394992892
- Semantic Scholar
- 6de4082d3b1631d1b2b5a38f611a23f5fbbe0d7d
- Cited by
- 90
- References
- 79
- Citations by year
- 2026: 15 · 2025: 52 · 2024: 23
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- large language models, single guide RNA design, human genome editing, base editing, precision genome editing
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