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Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data
The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehe...
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Published in: | Nature communications 2022-10, Vol.13 (1), p.6474-8, Article 6474 |
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Main Authors: | , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement.
Cas9 proteins require a target-adjacent sequence, the PAM, in order to cleave DNA. Here the authors develop a pipeline to accurately predict PAM sequences in order to facilitate the identification of Cas9s targeting specific sequences, including mutations. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-022-34213-9 |