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

CysHis 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.

Engineered dual selection for directed evolution of SpCas9 PAM specificity.

The widely used Streptococcus pyogenes Cas9 (SpCas9) nuclease derives its DNA targeting specificity from protein-DNA contacts with protospacer adjacent motif (PAM) sequences, in addition to base-pairing interactions between its guide RNA and target DNA. Previous reports have established that the PAM specificity of SpCas9 can be altered via positive selection procedures for directed evolution or other protein engineering strategies. Here we exploit in vivo directed evolution systems that incorporate simultaneous positive and negative selection to evolve SpCas9 variants with commensurate or improved activity on NAG PAMs relative to wild type and reduced activity on NGG PAMs, particularly YGG PAMs.

The geometric influence on the Cys2His2 zinc finger domain and functional plasticity.

The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity.