Predicting prime editing efficiency and product purity by deep learning

Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human p...

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Vydáno v:	Nature biotechnology Ročník 41; číslo 8; s. 1151 - 1159
Hlavní autoři:	Mathis, Nicolas, Allam, Ahmed, Kissling, Lucas, Marquart, Kim Fabiano, Schmidheini, Lukas, Solari, Cristina, Balázs, Zsolt, Krauthammer, Michael, Schwank, Gerald
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Nature Publishing Group US 01.08.2023 Nature Publishing Group
Témata:	631/114/1305 631/114/2163 631/1647/1511 631/208/726 631/61/201/2110 Agriculture Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology CRISPR-Cas Systems - genetics Datasets Deep Learning Editing Gene Editing Genome editing Genomes Hepatocytes Humans Life Sciences Mutation Neural networks Neural Networks, Computer Optimization Recurrent neural networks RNA editing
ISSN:	1087-0156, 1546-1696, 1546-1696
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human pathogenic mutations that include base substitutions, insertions and deletions. Based on this dataset, we identified sequence context features that influence prime editing and trained PRIDICT (prime editing guide prediction), an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes with a Spearman’s R of 0.85 and 0.78 for intended and unintended edits, respectively. We validated PRIDICT on endogenous editing sites as well as an external dataset and showed that pegRNAs with high (>70) versus low (<70) PRIDICT scores showed substantially increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and for translational research applications. The design of prime editing guide RNAs is optimized by deep learning.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1087-0156 1546-1696 1546-1696
DOI:	10.1038/s41587-022-01613-7