A universal deep-learning model for zinc finger design enables transcription factor reprogramming.
David M IchikawaOsama AbdinNader AlerasoolManjunatha KogenaruApril L MuellerHan WenDavid O GigantiGregory W GoldbergSamantha AdamsJeffrey M SpencerRozita RazaviSatra NimHong ZhengCourtney GioncoFinnegan T ClarkAlexey StrokachTimothy R HughesTimothée LionnetMikko TaipalePhilip M KimMarcus B NoyesPublished in: Nature biotechnology (2023)
Cys 2 His 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. 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 versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. 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.
Keyphrases
- transcription factor
- deep learning
- genome wide identification
- machine learning
- gene expression
- genome wide
- circulating tumor
- artificial intelligence
- dna binding
- cell free
- single molecule
- endothelial cells
- dna methylation
- convolutional neural network
- copy number
- oxide nanoparticles
- social media
- circulating tumor cells
- nucleic acid
- big data
- crispr cas
- genome wide analysis