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Rapid customization of base editors via machine learning-powered combinatorial mutagenesis.

Molecular cell - 21 May 2026

Peng Jiaxing, Chan Don C T, Chu Hoi Yee, Fong John H C, Lam Yin Kau, Cheung Maggie S H, Leung Wing, Choi Gigi C G, Wong Alan S L

Abstract excerpt

Being able to control the complementarity and hindrance between target DNA and base editor proteins enables precise, bystander-free editing. Here, we combined combinatorial mutagenesis with machine learning to analyze and engineer these interactions at scale. By profiling DNA motif preferences across 160,000 evoAPOBEC1 and 64 million TadA variants in human cells, we used as little as 0.004% of the mutational...

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CRISPR-Cas Systems
DNA
Gene Editing
HEK293 Cells
Machine Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Nucleotide Motifs

Predictive Learning Models

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PMID: 41997156
DOI: 10.1016/j.molcel.2026.03.030

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

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