A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

ABSTRACT: Cys₂His₂ 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. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. 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. 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. We demonstrate the

SUBMITTER: Ichikawa DM 

PROVIDER: S-EPMC10421740 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature