![]() ![]() ![]() While CRISPR–Cas and transcription activator-like effector (TALE)-based tools have been developed for such applications 4, 5, 6, 7, 8, 9, their intrinsic characteristics could limit their therapeutic efficacy. Diseases caused by haploinsufficiency, gain-of-function mutations or misexpression of a gene can be directly treated by modification of gene expression 1, 2, 3. Programmable regulation of gene expression would offer both powerful research tools as well as enormous therapeutic potential. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. Here we describe the screening of 49 billion protein–DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ![]() However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Nature Biotechnology volume 41, pages 1117–1129 ( 2023) Cite this articleĬys 2His 2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. A universal deep-learning model for zinc finger design enables transcription factor reprogramming ![]()
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