Search

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 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.

The interaction between the HSP90 chaperone and its client kinases is sensitive to the conformational status of the kinase, and stabilization of the kinase fold by small molecules strongly decreases chaperone interaction. Here we exploit this observation and assay small-molecule binding to kinases in living cells, using chaperones as 'thermodynamic sensors'. The method allows determination of target specificities of both ATP-competitive and allosteric inhibitors in the kinases' native cellular context in high throughput. We profile target specificities of 30 diverse kinase inhibitors against300 kinases. Demonstrating the value of the assay, we identify ETV6-NTRK3 as a target of the FDA-approved drug crizotinib (Xalkori). Crizotinib inhibits proliferation of ETV6-NTRK3-dependent tumor cells with nanomolar potency and induces the regression of established tumor xenografts in mice. Finally, we show that our approach is applicable to other chaperone and target classes by assaying HSP70/steroid hormone receptor and CDC37/kinase interactions, suggesting that chaperone interactions will have broad application in detecting drug-target interactions in vivo.