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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Bibliographic Details
Published in:Nature biotechnology Vol. 41; no. 8; pp. 1151 - 1159
Main Authors: Mathis, Nicolas, Allam, Ahmed, Kissling, Lucas, Marquart, Kim Fabiano, Schmidheini, Lukas, Solari, Cristina, Balázs, Zsolt, Krauthammer, Michael, Schwank, Gerald
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.08.2023
Nature Publishing Group
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-022-01613-7