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A general theoretical framework to design base editors with reduced bystander effects

Medicine and Health

A general theoretical framework to design base editors with reduced bystander effects

Q. Wang, J. Yang, et al.

Discover how a team of researchers, including Qian Wang and Jie Yang, is revolutionizing base editing techniques by developing a computational model that predicts and minimizes bystander effects, paving the way for more precise gene therapies.

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Playback language: English
Abstract
Base editors (BEs) are promising gene therapy tools, but their precision is limited by bystander effects. This study presents a discrete-state stochastic model, combined with molecular dynamics simulations, to predict and reduce bystander effects in BEs. The model successfully reproduces experimental data and guides the design of mutations to improve BE selectivity. Experimental verification confirms the model's predictions, providing a computational platform for designing more precise BEs.
Publisher
Nature Communications
Published On
Nov 11, 2021
Authors
Qian Wang, Jie Yang, Zhicheng Zhong, Jeffrey A. Vanegas, Xue Gao, Anatoly B. Kolomeisky
Tags
base editors
gene therapy
precision
bystander effects
computational model
experimental verification
mutations

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