Structure and engineering of the type III-E CRISPR-Cas7-11 effector complex

Kato, Kazuki; Zhou, Wenyuan; Okazaki, Sae; Isayama, Yukari; Nishizawa, Takashi; Gootenberg, Jonathan S.; Abudayyeh, Omar O.; Nishimasu, Hiroshi

doi:10.1016/j.cell.2022.05.003

Cited by 61 publications

(102 citation statements)

References 46 publications

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“…Rather than a minor filament of repeating Cas11 subunits extending towards the top of the complex, type III-Dv utilizes two separate small subunit domains that extend from different domains into the minor filament. Together, these data clearly define the structural similarities of the III-Dv domains with known type III-A and III-B structures, despite many of these proteins being fused and the large insertion in the last Cas7 domain, as seen in the type III-E system 1 .…”

Section: Resultssupporting

confidence: 53%

“…Another key difference is the presence of the Cas7-insertion subunit protruding from the effector complex, which has only been seen in the recent structure of the single polypeptide type III-E CRISPR-Cas effector 1 . In our structure, we found that the insertion domain within the Cas7-insertion subunit caps the 3’ end of the crRNA through base-stacking between F307 and A37 of the crRNA (Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

“…While this manuscript was in preparation, a manuscript describing the structure of the type III-E effector was published 1 . We reference these important findings; however, a careful comparison of the structures will follow once the coordinates have been released by the PDB.…”

Section: Important Notementioning

confidence: 99%

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Assembly of multi-subunit fusion proteins into the RNA-targeting type III-D CRISPR-Cas effector complex

Schwartz

Bravo

Macias

et al. 2022

Preprint

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CRISPR (Clustered regularly interspaced short palindromic repeats)-Cas (CRISPR-associated) systems are a type of adaptive immune response in bacteria and archaea that utilize crRNA (CRISPR RNA)-guided effector complexes to target complementary RNA or DNA for destruction. The prototypical type III-A and III-B CRISPR-Cas systems utilize multi-subunit effector complexes composed of individual proteins to cleave ssRNA targets at 6-nt intervals, as well as non-specifically degrading ssDNA and activating cyclic oligoadenylate (cOA) synthesis. Recent studies have shown that type III systems can contain subunit fusions yet maintain canonical type III RNA-targeting capabilities. To understand how a multi-subunit fusion effector functions, we determine structures of a variant type III-D effector and biochemically characterize how it cleaves RNA targets. These findings provide insights into how multi-subunit fusion proteins are tethered together and assemble into an active and programmable RNA endonuclease, how the effector utilizes a novel mechanism for target RNA seeding, and the structural basis for the evolution of type III effector complexes. Furthermore, our results provide a blueprint for fusing subunits in class 1 effectors for design of user-defined effector complexes with disparate activities.

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Section: Resultssupporting

confidence: 53%

Section: Resultsmentioning

confidence: 99%

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Assembly of multi-subunit fusion proteins into the RNA-targeting type III-D CRISPR-Cas effector complex

Schwartz

Bravo

Macias

et al. 2022

Preprint

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“…In a second head-to-head comparison, we tested DjCas13d against the recently reported Cas7-11 enzyme, which does not belong to the Cas13 family of CRISPR enzymes and was reported to have no impact on cell viability due to its distinct RNA cleavage mechanism (Kato et al, 2022; Özcan et al, 2021). We demonstrate that both enzymes have a comparable impact on cell viability and proliferation when targeting the same highly expressed transcripts.…”

Section: Resultsmentioning

confidence: 99%

“…• Random forest (RF): number of trees - [100,200,400,800,1000,1200,1500,1800,2000], number of features to consider when looking for the best split -all, sqrt(n_features), log2(n_features). • Long short-term memory recurrent neural network (LSTM): LSTM units - [16,32,64,128], dense layer units - [8,16,32], recurrent dense layer number -[0,1,2,3], dropout rate -[0.0, 0.1, 0.25]…”

Section: Model Training Hyperparameter Tuning and Evaluationmentioning

confidence: 99%

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting

Wei

Lotfy

Faizi

et al. 2021

Preprint

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Transcriptome engineering requires flexible RNA-targeting technologies that can perturb mammalian transcripts in a robust and scalable manner. CRISPR systems that natively target RNA molecules, such as Cas13 enzymes, are enabling rapid progress in the investigation of RNA biology and advancement of RNA therapeutics. Here, we sought to develop a Cas13 platform for high-throughput phenotypic screening and elucidate the design principles underpinning its RNA targeting efficiency. We employed the RfxCas13d (CasRx) system in a positive selection screen by tiling 55 known essential genes with single nucleotide resolution. Leveraging this dataset of over 127,000 guide RNAs, we systematically compared a series of linear regression and machine learning algorithms to train a convolutional neural network (CNN) model that is able to robustly predict guide RNA performance based on guide sequence alone. We further incorporated secondary features including secondary structure, free energy, target site position, and target isoform percent. To evaluate model performance, we conducted orthogonal screens via cell surface protein knockdown. The final CNN model is able to predict highly effective guide RNAs (gRNAs) within each transcript with >90% accuracy in this independent test set. To provide user interpretability, we evaluate feature contributions using both integrated gradients and SHapley Additive exPlanations (SHAP). We identify a specific sequence motif at guide position 15-24 along with selected secondary features to be predictive of highly efficient guides. Taken together, we derive Cas13d guide design rules from large-scale screen data, release a guide design tool (http://rnatargeting.org) to advance the RNA targeting toolbox, and describe a path for systematic development of deep learning models to predict CRISPR activity.

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