Structural basis of target DNA recognition by CRISPR-Cas12k for RNA-guided DNA transposition

Xiao, Renjian; Wang, Shukun; Han, Ruijie; Li, Zhuang; Gabel, C.; Mukherjee, Indranil Arun; Chang, Leifu

doi:10.1101/2021.07.07.451486

Cited by 2 publications

(1 citation statement)

References 61 publications

(60 reference statements)

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“…There are other variants of Cas proteins that are being investigated for this purpose. Some examples are CRISPR-Cas12 (Xiao et al, 2021;Senthilnathan et al, 2023), CRISPR-Cas13 (Kavuri et al, 2022), CRISPR-Cas3 (Morisaka et al, 2019), and many others. Therefore, the role of AI approaches should become more important.…”

Section: Introductionmentioning

confidence: 99%

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Dixit,

Kumar,

Srinivasan

et al. 2024

Front. Bioeng. Biotechnol.

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Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients’ genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer’s, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI’s contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

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Section: Introductionmentioning

confidence: 99%

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Dixit,

Kumar,

Srinivasan

et al. 2024

Front. Bioeng. Biotechnol.

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Structures of the holo CRISPR RNA-guided transposon integration complex

Park

Tsai

Rizo

et al. 2022

Preprint

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CRISPR-associated transposons (CAST) are programmable mobile elements that insert large DNA cargo by an RNA-guided mechanism. Multiple conserved components act in concert at the target site through formation of an integration complex (transpososome). We reconstituted the type V-K CAST transpososome fromScytonema hofmannii(ShCAST) and determined the structure using cryo-EM. Transpososome architecture ensures orientation-specific association: AAA+ regulator TnsC has defined polarity and length, with dedicated interaction interfaces for other CAST components. Interestingly, transposase (TnsB)-TnsC interactions we observe contribute to stimulating TnsC's ATP hydrolysis activity. TnsC deviates from previously observed helical configurations of TnsC, and target DNA does not track with TnsC protomers. Consequently, TnsC makes new, functionally important protein-DNA interactions throughout the transpososome. Finally, two distinct transpososome populations suggests that associations with the CRISPR effector are flexible. These ShCAST transpososome structures significantly enhances our understanding of CAST transposition systems and suggests avenues for improving CAST transposition for precision genome-editing applications.

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Structural basis of target DNA recognition by CRISPR-Cas12k for RNA-guided DNA transposition

Cited by 2 publications

References 61 publications

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Structures of the holo CRISPR RNA-guided transposon integration complex

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