Deep learning models to predict the editing efficiencies and outcomes of diverse base editors.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
29
09
2022
accepted:
13
04
2023
medline:
18
3
2024
pubmed:
16
5
2023
entrez:
15
5
2023
Statut:
ppublish
Résumé
Applications of base editing are frequently restricted by the requirement for a protospacer adjacent motif (PAM), and selecting the optimal base editor (BE) and single-guide RNA pair (sgRNA) for a given target can be difficult. To select for BEs and sgRNAs without extensive experimental work, we systematically compared the editing windows, outcomes and preferred motifs for seven BEs, including two cytosine BEs, two adenine BEs and three C•G to G•C BEs at thousands of target sequences. We also evaluated nine Cas9 variants that recognize different PAM sequences and developed a deep learning model, DeepCas9variants, for predicting which variants function most efficiently at sites with a given target sequence. We then develop a computational model, DeepBE, that predicts editing efficiencies and outcomes of 63 BEs that were generated by incorporating nine Cas9 variants as nickase domains into the seven BE variants. The predicted median efficiencies of BEs with DeepBE-based design were 2.9- to 20-fold higher than those of rationally designed SpCas9-containing BEs.
Identifiants
pubmed: 37188916
doi: 10.1038/s41587-023-01792-x
pii: 10.1038/s41587-023-01792-x
doi:
Substances chimiques
CRISPR-Associated Protein 9
EC 3.1.-
RNA, Guide, CRISPR-Cas Systems
0
BES
10191-18-1
Alkanesulfonic Acids
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
484-497Subventions
Organisme : National Research Foundation of Korea (NRF)
ID : 2022R1A3B1078084
Organisme : National Research Foundation of Korea (NRF)
ID : 2018R1A5A2025079
Organisme : National Research Foundation of Korea (NRF)
ID : 2022M3A9E4017127
Organisme : National Research Foundation of Korea (NRF)
ID : 2022M3A9F3017506
Organisme : National Research Foundation of Korea (NRF)
ID : 2022R1C1C2004229
Organisme : Ministry of Trade, Industry and Energy (Ministry of Trade, Industry and Energy, Korea)
ID : HN21C0917 (H.H.K.))
Organisme : Ministry of Health and Welfare (Ministry of Health, Welfare and Family Affairs)
ID : HI21C1314
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
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