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Novel CRISPR-Cas12a Clades Discovery Using Large Language Model
Open accessPreprint — published as 10.1038/s41467-025-63160-4 · Paper page
Abstract
The identification and engineering of CRISPR-Cas systems revolutionized life science. Metagenome contains millions of unknown Cas proteins, which require precise prediction and characterization. Traditional protein mining mainly depends on protein sequence alignments. In this work, we harnessed the capability of the evolutionary scale language model (ESM) to learn the information beyond the sequence. After training with the CRISPR-Cas sequences and their functional annotation, the ESM model can identify the CRISPR-Cas proteins from the annotated genome sequences accurately and robustly without sequence alignment. However, due to the lack of experimental data, the feature prediction is limited by the small sample size. Integrated with machine learning on small size experimental data, the model is able to predict the trans-cleavage activity of novel Cas12a. Furthermore, we discovered 7 novel subtypes of Cas12a proteins with unique organization of CRISPR loci and protein sequences. Notably, structural alignments revealed that Cas1, Cas2, and Cas4 also exhibit 8 subtypes, with the absence of integrase proteins correlating with a reduction in spacer numbers within CRISPR loci. In addition, the Cas12a subtypes displayed distinct 3D foldings, a finding further corroborated by CryoEM analyses that unveiled unique interaction patterns with RNA. Accordingly, these proteins show distinct double-strand and single-strand DNA cleavage preferences and broad PAM recognition. Finally, we established a specific detection strategy for the oncogene SNP without traditional Cas12a PAM. This study shows the great potential of the language model in the novel Cas protein function exploration via gene cluster classification.
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Huang, X., Feng, Y., Shi, J., Yang, J., Li, Z., Li, Y., Tang, J., Yang, Y., Zheng, J., Han, W., Huang, S., Zhang, J., Qiao, Y., Ma, P., Liu, Q., Wu, L., Hu, C., & Zhang, X.
- Affiliations
- ShanghaiTech University (CN)
- Zhejiang Lab (CN)
- Zhejiang Lab
- State Key Laboratory of Reproductive Medicine (CN)
- Shanghai Jiao Tong University (CN)
- Tongji University (CN)
- Nanjing Normal University (CN)
- National University of Singapore (SG)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Huang, Xingxu
- Tang, Jin
- Ma, Peixiang
- Zhang, Xiaokang
- ORCID
- Venue
- Nature Communications
- Published
- 2024-08-06
- Volume
- 16
- Issue
- 1
- Pages
- 7877-7877
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12375029
- OpenAlex
- W4413470266
- Cited by
- 21
- References
- 1
- Citations by year
- 2026: 16 · 2025: 5
- FWCI
- 6.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Shanghai Science and Technology Development Foundation (24141901302)
- National Natural Science Foundation of China (BK20233003, 22177073, 32161133022)
- Chinese Academy of Sciences (ZDBS-ZRKJZ-TLC008)
- Science and Technology Commission of Shanghai Municipality (23HC1400700)
- University of Science and Technology of China
- ShanghaiTech University
- National Institutes of Health
- University of California, San Francisco (P41-GM103311)
- National Institute of General Medical Sciences (P41GM103311)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- CRISPR/Cas12a, protein function prediction, DNA cleavage specificity, genome editing
- MeSH terms
- Machine Learning, Large Language Models, Bacterial Proteins, Endodeoxyribonucleases, Cryoelectron Microscopy, Polymorphism, Single Nucleotide, CRISPR-Cas Systems, CRISPR-Associated Proteins
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