RNA-protein interaction mapping via MS2 or Cas13-based APEX targeting

Han, Shuo; Zhao, Boxuan Simen; Myers, Samuel A.; Carr, Steven A.; He, Chuan; Ting, Alice Y.

doi:10.1101/2020.02.27.968297

Cited by 16 publications

(24 citation statements)

References 88 publications

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“…Further, their modular nature enables the facile fusion of effector domains to expand effector functionality. As a result, a broad suite of Cas13-based tools is now able to perturb RNA expression (Abudayyeh et al, 2017; Konermann et al, 2018) or splicing (Konermann et al, 2018), mediate RNA editing (Abudayyeh et al, 2019; Cox et al, 2017; Xu et al, 2021) or methylation (Wilson et al, 2020), as well as profile RNA-protein interactions (Han et al, 2020). These capabilities are now accelerating applications across the study of fundamental RNA biology, RNA-based therapeutics, and molecular diagnostics.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

confidence: 99%

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

Wei

Lotfy

Faizi

et al. 2021

Preprint

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“…Especially when combined with functional assays and screens, split-TurboID-based PL can be a powerful tool for biological discovery around organelle contact sites or macromolecular complexes. Split-TurboID may also improve signal-tonoise for challenging targeting applications, such as dCas9-directed PL of specific genomic loci (49,50), or dCas13-directed PL of specific cellular RNAs (51).…”

Section: Discussionmentioning

confidence: 99%

Split-TurboID enables contact-dependent proximity labeling in cells

Cho

Branon

Rajeev

et al. 2020

Preprint

Self Cite

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Proximity labeling (PL) catalyzed by promiscuous enzymes such as TurboID have enabled the proteomic analysis of subcellular regions difficult or impossible to access by conventional fractionation-based approaches. Yet some cellular regions, such as organelle contact sites, remain out of reach for current PL methods. To address this limitation, we split the enzyme TurboID into two inactive fragments that recombine when driven together by a protein-protein interaction or membrane-membrane apposition. At endoplasmic reticulum (ER)-mitochondria contact sites, reconstituted TurboID catalyzed spatially-restricted biotinylation, enabling the enrichment and identification of >100 endogenous proteins, including many not previously linked to ER-mitochondria contacts. We validated eight novel candidates by biochemical fractionation and overexpression imaging. Overall, split-TurboID is a versatile tool for conditional and spatially-specific proximity labeling in cells.

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“…Cas13, a type Ⅳ CRISPR/Cas system protein, assembles with crispr RNA (crRNA) and forms a crRNA-guided RNA targeting effector complex to directly cut RNA (157). Similarly, catalytically dead RCas9 (dRCas9) and Cas13 (dCas13) have been used as RNA-targeting tools to image RNA and to identify interaction proteins of RNA (158,159). Recently, combining with m 6 A writers or erasers, these two systems are successfully applied into programming RNA modification (Fig.…”

Section: Programmable M 6 a Editing Based On M 6 A Writers/erasers-conjugated Crispr/cas Systemmentioning

confidence: 99%

The detection and functions of RNA modification m6A based on m6A writers and erasers

Zhang

Yang

Jia

2021

Journal of Biological Chemistry

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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RNA-protein interaction mapping via MS2 or Cas13-based APEX targeting

Cited by 16 publications

References 88 publications

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

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

Split-TurboID enables contact-dependent proximity labeling in cells

The detection and functions of RNA modification m6A based on m6A writers and erasers

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