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Updated: Jul 2, 2025

A Nonsequencing Approach for the Rapid Detection of RNA Editing
Published on: April 21, 2022
Protein language models-assisted optimization of a uracil-N-glycosylase variant enables programmable T-to-G and
Yan He1, Xibin Zhou2, Chong Chang3
1Fudan University, 220 Handan Road, Shanghai 200433, China; School of Medicine, Westlake University, Hangzhou, Zhejiang 310014, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310014, China; Research Center for Industries of the Future (RCIF), Westlake University, Hangzhou, Zhejiang 310014, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310014, China; Westlake Center for Genome Editing, Westlake Laboratory of Life Sciences and Biomedicine, Westlake University, 18 Shilongshan Road, Hangzhou, Zhejiang 310024, China.
Abstract:
Current base editors (BEs) use DNA deaminases, including cytidine deaminase in cytidine BE (CBE) or adenine deaminase in adenine BE (ABE), to facilitate transition nucleotide substitutions. Combining CBE or ABE with glycosylase enzymes can induce limited transversion mutations. Nonetheless, a critical demand remains for BEs capable of generating alternative mutation types, such as T>G corrections. In this study, we leveraged pre-trained protein language models to optimize a uracil-N-glycosylase (UNG) variant with altered specificity for thymines (eTDG). Notably, after two rounds of testing fewer than 50 top-ranking variants, more than 50% exhibited over 1.5-fold enhancement in enzymatic activities. When eTDG was fused with nCas9, it induced programmable T-to-S (G/C) substitutions and corrected db/db diabetic mutation in mice (up to 55%). Our findings not only establish orthogonal strategies for developing novel BEs but also demonstrate the capacities of protein language models for optimizing enzymes without extensive task-specific training data.
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