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Cited by 21CRISPR and Genetic EngineeringGenomics and Phylogenetic StudiesBiochemical and Structural Characterization

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Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models

Open access
Li, Wenhui, Jiang, Xianyue, Wang, Wuke, Hou, Liya, Cai, Runze, Li, Yongqian, Gu, Qiuxi, Chen, Qinchang, Ma, Peixiang, Tang, Jin, Guo, Menghao, Chuai, Guohui, Huang, Xingxu, Zhang, Jun, Liu, Qi·2024·Nature Communications
DOI 10.1038/s41467-024-54365-0PMC PMC11576732

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Abstract

Abstract The discovery and functional annotation of CRISPR-Cas systems laid the groundwork for the development of novel CRISPR-based gene editing tools. Traditional similarity- search-based Cas discovery strategies, which rely heavily on local sequence alignment and reference Cas homologs, may overlook a significant number of remote homologs with limited sequence similarity; and it can not be applied directly for functional recognition. With the rapid development of protein large language models (LLMs), protein foundation models are expected to help model Cas systems with limited Cas homologs without extensive task-specific training data; however, the full potential of these models for Cas discovery and functional annotation has yet to be determined. To this end, we present a novel, effective and unified AI framework, CHOOSER ( C as HO mlog O bserving and SE lf-processing sc R eening), for alignment-free discovery of novel CRISPR-Cas systems with self-processing precursor CRISPR RNA (pre-crRNA) capability utilizing protein foundation models. CHOOSER successfully retrieved 11 novel homologs of Casλ, the majority of which are predicted to be able to self-process pre-crRNA, nearly doubling the current catalog. One of the candidates, EphcCasλ, was subsequently experimentally validated for its ability to self-process pre-crRNA, target DNA cleavage, and trans-cleavage and was shown to be a promising candidate for use as a CRISPR-Cas-based pathogen detection system. Overall, our study provides an unprecedented perspective and methodology for discovering novel CRISPR-Cas systems with specific functions using foundation models, underscoring the potential for transforming newly identified Cas homologs into genetic editing tools.

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  1. Wang, J. Y. & Doudna, J. A. CRISPR technology: a decade of genome editing is only the beginning. Science 379, eadd8643 (2023). · DOI
  2. Shmakov, S. et al. Diversity and evolution of class 2 CRISPR–Cas systems. Nat. Rev. Microbiol 15, 169–182 (2017). · DOI · Paper page
  3. Burstein, D. et al. New CRISPR–Cas systems from uncultivated microbes. Nature 542, 237–241 (2017). · DOI
  4. Harrington, L. B. et al. Programmed DNA destruction by miniature CRISPR-Cas14 enzymes. Science 362, 839–842 (2018). · DOI
  5. Yan, W. X. et al. Functionally diverse type V CRISPR-Cas systems. Science 363, 88–91 (2019). · DOI
  6. Pausch, P. et al. CRISPR-CasΦ from huge phages is a hypercompact genome editor. Science 369, 333–337 (2020). · DOI
  7. Al-Shayeb, B. et al. Diverse virus-encoded CRISPR-Cas systems include streamlined genome editors. Cell 185, 4574–4586.e16 (2022). · DOI
  8. Wu, W. Y. et al. The miniature CRISPR-Cas12m effector binds DNA to block transcription. Mol. Cell 82, 4487–4502.e7 (2022). · DOI
  9. Sun, A. et al. The compact Casπ (Cas12l) ‘bracelet’ provides a unique structural platform for DNA manipulation. Cell Res. https://doi.org/10.1038/s41422-022-00771-2 (2023). · DOI
  10. East-Seletsky, A. et al. Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection. Nature 538, 270–273 (2016). · DOI · Paper page
  11. East-Seletsky, A., O’Connell, M. R., Burstein, D., Knott, G. J. & Doudna, J. A. RNA targeting by functionally orthogonal type VI-A CRISPR-Cas enzymes. Mol. Cell 66, 373–383.e3 (2017). · DOI · Paper page
  12. Yan, W. X. et al. Cas13d is a compact RNA-targeting type VI CRISPR effector positively modulated by a WYL-domain-containing accessory protein. Mol. Cell 70, 327–339.e5 (2018). · DOI
  13. Xu, C. et al. Programmable RNA editing with compact CRISPR–Cas13 systems from uncultivated microbes. Nat. Methods 18, 499–506 (2021). · DOI · Paper page
  14. Fonfara, I., Richter, H., Bratovič, M., Le Rhun, A. & Charpentier, E. The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA. Nature 532, 517–521 (2016). · DOI · Paper page
  15. Zhang, H., Li, Z., Xiao, R. & Chang, L. Mechanisms for target recognition and cleavage by the Cas12i RNA-guided endonuclease. Nat. Struct. Mol. Biol. 27, 1069–1076 (2020). · DOI
  16. Campa, C. C., Weisbach, N. R., Santinha, A. J., Incarnato, D. & Platt, R. J. Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts. Nat. Methods 16, 887–893 (2019). · DOI
  17. McGaw, C. et al. Engineered Cas12i2 is a versatile high-efficiency platform for therapeutic genome editing. Nat. Commun. 13, 2833 (2022). · DOI
  18. Tatusova, T. et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 44, 6614–6624 (2016). · DOI
  19. Russel, J., Pinilla-Redondo, R., Mayo-Muñoz, D., Shah, S. A. & Sørensen, S. J. CRISPRCasTyper: automated identification, annotation, and classification of CRISPR-Cas Loci. CRISPR J. 3, 462–469 (2020). · DOI
  20. Ye, J., McGinnis, S. & Madden, T. L. BLAST: improvements for better sequence analysis. Nucleic Acids Res. 34, W6–W9 (2006). · DOI
  21. Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011). · DOI · Paper page
  22. Altae-Tran, H. et al. Uncovering the functional diversity of rare CRISPR-Cas systems with deep terascale clustering. Science 382, eadi1910 (2023). · DOI
  23. Makarova, K. S. et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat. Rev. Microbiol. 18, 67–83 (2020). · DOI · Paper page
  24. Adler, B. A. et al. CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes. Nucleic Acids Research 52, D590–D596 (2024). · DOI
  25. Hsu, C. et al. Learning Inverse Folding from Millions of Predicted Structures. http://biorxiv.org/lookup/doi/10.1101/2022.04.10.487779 (2022). · DOI
  26. Vig, J. et al. BERTology Meets Biology: Interpreting Attention in Protein Language Models. Preprint at http://arxiv.org/abs/2006.15222 (2021). · DOI · Paper page
  27. Omura, S. N. et al. Mechanistic and evolutionary insights into a type V-M CRISPR–Cas effector enzyme. Nat. Struct. Mol. Biol. 30, 1172–1182 (2023). · DOI
  28. Chen, J. S. et al. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science 360, 436–439 (2018). · DOI · Paper page
  29. Flamholz, Z. N., Biller, S. J. & Kelly, L. Large language models improve annotation of prokaryotic viral proteins. Nat. Microbiol. https://doi.org/10.1038/s41564-023-01584-8 (2024). · DOI · Paper page
  30. Richardson, L. et al. MGnify: the microbiome sequence data analysis resource in 2023. Nucleic Acids Res. 51, D753–D759 (2023). · DOI
  31. Coelho, L. P. et al. Towards the biogeography of prokaryotic genes. Nature 601, 252–256 (2022). · DOI
  32. Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021). · DOI
  33. Zhu, J. et al. Over 50,000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa. Genom. Proteom. Bioinform. 20, 246–259 (2022). · DOI
  34. Liu, Y. et al. A genome and gene catalog of glacier microbiomes. Nat. Biotechnol. 40, 1341–1348 (2022). · DOI
  35. Camargo, A. P. et al. IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata. Nucleic Acids Res. 51, D733–D743 (2023). · DOI · Paper page
  36. Nayfach, S. et al. Metagenomic compendium of 189,680 DNA viruses from the human gut microbiome. Nat. Microbiol. 6, 960–970 (2021). · DOI · Paper page
  37. Soto-Perez, P. et al. CRISPR-Cas system of a prevalent human gut bacterium reveals hyper-targeting against phages in a human virome catalog. Cell Host Microbe 26, 325–335.e5 (2019). · DOI
  38. Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D. & Lawley, T. D. Massive expansion of human gut bacteriophage diversity. Cell 184, 1098–1109.e9 (2021). · DOI · Paper page
  39. Cook, R. et al. INfrastructure for a PHAge REference Database: identification of large-scale biases in the current collection of cultured phage genomes. Phage 2, 214–223 (2021). · DOI
  40. Mihara, T. et al. Linking virus genomes with host taxonomy. Viruses 8, 66 (2016). · DOI
  41. Schmartz, G. P. et al. PLSDB: advancing a comprehensive database of bacterial plasmids. Nucleic Acids Res. 50, D273–D278 (2022). · DOI
  42. Pickard, D. et al. Molecular characterization of the Salmonella enterica serovar typhi Vi-typing bacteriophage E1. J. Bacteriol. 190, 2580–2587 (2008). · DOI
  43. Bland, C. et al. CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinform. 8, 209 (2007). · DOI
  44. Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109–1115 (2022). · DOI · Paper page
  45. Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009). · DOI · Paper page
  46. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013). · DOI · Paper page
  47. Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015). · DOI · Paper page
  48. Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021). · DOI
  49. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011). · DOI · Paper page
  50. Karvelis, T. et al. PAM recognition by miniature CRISPR–Cas12f nucleases triggers programmable double-stranded DNA target cleavage. Nucleic Acids Res. 48, 5016–5023 (2020). · DOI

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Metadata

Authors
Li, W., Jiang, X., Wang, W., Hou, L., Cai, R., Li, Y., Gu, Q., Chen, Q., Ma, P., Tang, J., Guo, M., Chuai, G., Huang, X., Zhang, J., & Liu, Q.
Affiliations
  • Tongji University (CN)
  • Zhejiang Lab (CN)
  • Nanjing Maternity and Child Health Care Hospital (CN)
  • Shanghai Jiao Tong University (CN)
Corresponding author
  • Chuai, Guohui
  • Huang, Xingxu
  • Zhang, Jun
  • Liu, Qi
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2024-11-19
Volume
15
Issue
1
Pages
10024-10024
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-024-54365-0
PMC
PMC11576732
OpenAlex
W4404525423
Cited by
21
References
8
Citations by year
2026: 4 · 2025: 16 · 2024: 1
FWCI
2.86
Type
journal article
Language
en
Update notices
correction (2025-01-09)
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Natural Science Foundation of China (62088101, T24250193, 2021SHZDZX0100, 32341008, 62002265)
  • National Key Research and Development Program of China (2021SHZDZX0100, 2021YFF1200900, 2021YFF1201200, 2022YFC2702705, 62088101)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biochemical and Structural Characterization
Keywords
CRISPR-Cas, large language models, DNA cleavage, trans-cleavage, genome editing
MeSH terms
Gene Editing, DNA Cleavage, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems, CRISPR-Associated Proteins

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