Predicting adenine base editing efficiencies in different cellular contexts by deep learning.

ABSTRACT:

Background

Adenine base editors (ABEs) enable the conversion of A•T to G•C base pairs. Since the sequence of the target locus influences base editing efficiency, efforts have been made to develop computational models that can predict base editing outcomes based on the targeted sequence. However, these models were trained on base editing datasets generated in cell lines and their predictive power for base editing in primary cells in vivo remains uncertain.

Results

In this study, we conduct base editing screens using SpRY-ABEmax and SpRY-ABE8e to target 2,195 pathogenic mutations with a total of 12,000 guide RNAs in cell lines and in the murine liver. We observe strong correlations between in vitro datasets generated by ABE-mRNA electroporation into HEK293T cells and in vivo d

SUBMITTER: Kissling L 

PROVIDER: S-EPMC12060317 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature